481 lines
28 KiB
Markdown
481 lines
28 KiB
Markdown
Machine Learning Overview {#ml_intro}
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=========================
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[TOC]
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Training Data {#ml_intro_data}
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=============
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In machine learning algorithms there is notion of training data. Training data includes several
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components:
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- A set of training samples. Each training sample is a vector of values (in Computer Vision it's
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sometimes referred to as feature vector). Usually all the vectors have the same number of
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components (features); OpenCV ml module assumes that. Each feature can be ordered (i.e. its
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values are floating-point numbers that can be compared with each other and strictly ordered,
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i.e. sorted) or categorical (i.e. its value belongs to a fixed set of values that can be
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integers, strings etc.).
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- Optional set of responses corresponding to the samples. Training data with no responses is used
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in unsupervised learning algorithms that learn structure of the supplied data based on distances
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between different samples. Training data with responses is used in supervised learning
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algorithms, which learn the function mapping samples to responses. Usually the responses are
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scalar values, ordered (when we deal with regression problem) or categorical (when we deal with
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classification problem; in this case the responses are often called "labels"). Some algorithms,
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most noticeably Neural networks, can handle not only scalar, but also multi-dimensional or
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vector responses.
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- Another optional component is the mask of missing measurements. Most algorithms require all the
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components in all the training samples be valid, but some other algorithms, such as decision
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trees, can handle the cases of missing measurements.
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- In the case of classification problem user may want to give different weights to different
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classes. This is useful, for example, when:
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- user wants to shift prediction accuracy towards lower false-alarm rate or higher hit-rate.
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- user wants to compensate for significantly different amounts of training samples from
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different classes.
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- In addition to that, each training sample may be given a weight, if user wants the algorithm to
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pay special attention to certain training samples and adjust the training model accordingly.
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- Also, user may wish not to use the whole training data at once, but rather use parts of it, e.g.
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to do parameter optimization via cross-validation procedure.
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As you can see, training data can have rather complex structure; besides, it may be very big and/or
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not entirely available, so there is need to make abstraction for this concept. In OpenCV ml there is
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cv::ml::TrainData class for that.
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@sa cv::ml::TrainData
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Normal Bayes Classifier {#ml_intro_bayes}
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=======================
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This simple classification model assumes that feature vectors from each class are normally
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distributed (though, not necessarily independently distributed). So, the whole data distribution
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function is assumed to be a Gaussian mixture, one component per class. Using the training data the
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algorithm estimates mean vectors and covariance matrices for every class, and then it uses them for
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prediction.
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@sa cv::ml::NormalBayesClassifier
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K-Nearest Neighbors {#ml_intro_knn}
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===================
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The algorithm caches all training samples and predicts the response for a new sample by analyzing a
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certain number (__K__) of the nearest neighbors of the sample using voting, calculating weighted
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sum, and so on. The method is sometimes referred to as "learning by example" because for prediction
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it looks for the feature vector with a known response that is closest to the given vector.
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@sa cv::ml::KNearest
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Support Vector Machines {#ml_intro_svm}
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=======================
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Originally, support vector machines (SVM) was a technique for building an optimal binary (2-class)
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classifier. Later the technique was extended to regression and clustering problems. SVM is a partial
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case of kernel-based methods. It maps feature vectors into a higher-dimensional space using a kernel
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function and builds an optimal linear discriminating function in this space or an optimal hyper-
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plane that fits into the training data. In case of SVM, the kernel is not defined explicitly.
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Instead, a distance between any 2 points in the hyper-space needs to be defined.
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The solution is optimal, which means that the margin between the separating hyper-plane and the
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nearest feature vectors from both classes (in case of 2-class classifier) is maximal. The feature
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vectors that are the closest to the hyper-plane are called _support vectors_, which means that the
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position of other vectors does not affect the hyper-plane (the decision function).
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SVM implementation in OpenCV is based on @cite LibSVM
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@sa cv::ml::SVM
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Prediction with SVM {#ml_intro_svm_predict}
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-------------------
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StatModel::predict(samples, results, flags) should be used. Pass flags=StatModel::RAW_OUTPUT to get
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the raw response from SVM (in the case of regression, 1-class or 2-class classification problem).
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Decision Trees {#ml_intro_trees}
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==============
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The ML classes discussed in this section implement Classification and Regression Tree algorithms
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described in @cite Breiman84 .
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The class cv::ml::DTrees represents a single decision tree or a collection of decision trees. It's
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also a base class for RTrees and Boost.
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A decision tree is a binary tree (tree where each non-leaf node has two child nodes). It can be used
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either for classification or for regression. For classification, each tree leaf is marked with a
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class label; multiple leaves may have the same label. For regression, a constant is also assigned to
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each tree leaf, so the approximation function is piecewise constant.
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@sa cv::ml::DTrees
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Predicting with Decision Trees {#ml_intro_trees_predict}
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------------------------------
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To reach a leaf node and to obtain a response for the input feature vector, the prediction procedure
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starts with the root node. From each non-leaf node the procedure goes to the left (selects the left
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child node as the next observed node) or to the right based on the value of a certain variable whose
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index is stored in the observed node. The following variables are possible:
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- __Ordered variables.__ The variable value is compared with a threshold that is also stored in
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the node. If the value is less than the threshold, the procedure goes to the left. Otherwise, it
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goes to the right. For example, if the weight is less than 1 kilogram, the procedure goes to the
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left, else to the right.
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- __Categorical variables.__ A discrete variable value is tested to see whether it belongs to a
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certain subset of values (also stored in the node) from a limited set of values the variable
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could take. If it does, the procedure goes to the left. Otherwise, it goes to the right. For
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example, if the color is green or red, go to the left, else to the right.
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So, in each node, a pair of entities (variable_index , `decision_rule (threshold/subset)` ) is used.
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This pair is called a _split_ (split on the variable variable_index ). Once a leaf node is reached,
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the value assigned to this node is used as the output of the prediction procedure.
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Sometimes, certain features of the input vector are missed (for example, in the darkness it is
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difficult to determine the object color), and the prediction procedure may get stuck in the certain
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node (in the mentioned example, if the node is split by color). To avoid such situations, decision
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trees use so-called _surrogate splits_. That is, in addition to the best "primary" split, every tree
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node may also be split to one or more other variables with nearly the same results.
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Training Decision Trees {#ml_intro_trees_train}
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-----------------------
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The tree is built recursively, starting from the root node. All training data (feature vectors and
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responses) is used to split the root node. In each node the optimum decision rule (the best
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"primary" split) is found based on some criteria. In machine learning, gini "purity" criteria are
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used for classification, and sum of squared errors is used for regression. Then, if necessary, the
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surrogate splits are found. They resemble the results of the primary split on the training data. All
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the data is divided using the primary and the surrogate splits (like it is done in the prediction
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procedure) between the left and the right child node. Then, the procedure recursively splits both
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left and right nodes. At each node the recursive procedure may stop (that is, stop splitting the
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node further) in one of the following cases:
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- Depth of the constructed tree branch has reached the specified maximum value.
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- Number of training samples in the node is less than the specified threshold when it is not
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statistically representative to split the node further.
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- All the samples in the node belong to the same class or, in case of regression, the variation is
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too small.
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- The best found split does not give any noticeable improvement compared to a random choice.
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When the tree is built, it may be pruned using a cross-validation procedure, if necessary. That is,
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some branches of the tree that may lead to the model overfitting are cut off. Normally, this
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procedure is only applied to standalone decision trees. Usually tree ensembles build trees that are
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small enough and use their own protection schemes against overfitting.
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Variable Importance {#ml_intro_trees_var}
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-------------------
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Besides the prediction that is an obvious use of decision trees, the tree can be also used for
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various data analyses. One of the key properties of the constructed decision tree algorithms is an
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ability to compute the importance (relative decisive power) of each variable. For example, in a spam
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filter that uses a set of words occurred in the message as a feature vector, the variable importance
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rating can be used to determine the most "spam-indicating" words and thus help keep the dictionary
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size reasonable.
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Importance of each variable is computed over all the splits on this variable in the tree, primary
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and surrogate ones. Thus, to compute variable importance correctly, the surrogate splits must be
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enabled in the training parameters, even if there is no missing data.
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Boosting {#ml_intro_boost}
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========
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A common machine learning task is supervised learning. In supervised learning, the goal is to learn
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the functional relationship \f$F: y = F(x)\f$ between the input \f$x\f$ and the output \f$y\f$ .
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Predicting the qualitative output is called _classification_, while predicting the quantitative
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output is called _regression_.
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Boosting is a powerful learning concept that provides a solution to the supervised classification
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learning task. It combines the performance of many "weak" classifiers to produce a powerful
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committee @cite HTF01 . A weak classifier is only required to be better than chance, and thus can be
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very simple and computationally inexpensive. However, many of them smartly combine results to a
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strong classifier that often outperforms most "monolithic" strong classifiers such as SVMs and
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Neural Networks.
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Decision trees are the most popular weak classifiers used in boosting schemes. Often the simplest
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decision trees with only a single split node per tree (called stumps ) are sufficient.
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The boosted model is based on \f$N\f$ training examples \f${(x_i,y_i)}1N\f$ with \f$x_i \in{R^K}\f$
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and \f$y_i \in{-1, +1}\f$ . \f$x_i\f$ is a \f$K\f$ -component vector. Each component encodes a
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feature relevant to the learning task at hand. The desired two-class output is encoded as -1 and +1.
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Different variants of boosting are known as Discrete Adaboost, Real AdaBoost, LogitBoost, and Gentle
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AdaBoost @cite FHT98 . All of them are very similar in their overall structure. Therefore, this
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chapter focuses only on the standard two-class Discrete AdaBoost algorithm, outlined below.
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Initially the same weight is assigned to each sample (step 2). Then, a weak classifier
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\f$f_{m(x)}\f$ is trained on the weighted training data (step 3a). Its weighted training error and
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scaling factor \f$c_m\f$ is computed (step 3b). The weights are increased for training samples that
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have been misclassified (step 3c). All weights are then normalized, and the process of finding the
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next weak classifier continues for another \f$M\f$ -1 times. The final classifier \f$F(x)\f$ is the
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sign of the weighted sum over the individual weak classifiers (step 4).
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__Two-class Discrete AdaBoost Algorithm__
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- Set \f$N\f$ examples \f${(x_i,y_i)}1N\f$ with \f$x_i \in{R^K}, y_i \in{-1, +1}\f$ .
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- Assign weights as \f$w_i = 1/N, i = 1,...,N\f$ .
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- Repeat for \f$m = 1,2,...,M\f$ :
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- Fit the classifier \f$f_m(x) \in{-1,1}\f$, using weights \f$w_i\f$ on the training data.
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- Compute \f$err_m = E_w [1_{(y \neq f_m(x))}], c_m = log((1 - err_m)/err_m)\f$ .
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- Set \f$w_i \Leftarrow w_i exp[c_m 1_{(y_i \neq f_m(x_i))}], i = 1,2,...,N,\f$ and
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renormalize so that \f$\Sigma i w_i = 1\f$ .
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- Classify new samples _x_ using the formula: \f$\textrm{sign} (\Sigma m = 1M c_m f_m(x))\f$ .
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@note Similar to the classical boosting methods, the current implementation supports two-class
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classifiers only. For M \> 2 classes, there is the __AdaBoost.MH__ algorithm (described in
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@cite FHT98) that reduces the problem to the two-class problem, yet with a much larger training set.
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To reduce computation time for boosted models without substantially losing accuracy, the influence
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trimming technique can be employed. As the training algorithm proceeds and the number of trees in
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the ensemble is increased, a larger number of the training samples are classified correctly and with
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increasing confidence, thereby those samples receive smaller weights on the subsequent iterations.
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Examples with a very low relative weight have a small impact on the weak classifier training. Thus,
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such examples may be excluded during the weak classifier training without having much effect on the
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induced classifier. This process is controlled with the weight_trim_rate parameter. Only examples
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with the summary fraction weight_trim_rate of the total weight mass are used in the weak classifier
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training. Note that the weights for __all__ training examples are recomputed at each training
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iteration. Examples deleted at a particular iteration may be used again for learning some of the
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weak classifiers further @cite FHT98
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@sa cv::ml::Boost
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Prediction with Boost {#ml_intro_boost_predict}
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---------------------
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StatModel::predict(samples, results, flags) should be used. Pass flags=StatModel::RAW_OUTPUT to get
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the raw sum from Boost classifier.
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Random Trees {#ml_intro_rtrees}
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============
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Random trees have been introduced by Leo Breiman and Adele Cutler:
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<http://www.stat.berkeley.edu/users/breiman/RandomForests/> . The algorithm can deal with both
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classification and regression problems. Random trees is a collection (ensemble) of tree predictors
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that is called _forest_ further in this section (the term has been also introduced by L. Breiman).
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The classification works as follows: the random trees classifier takes the input feature vector,
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classifies it with every tree in the forest, and outputs the class label that received the majority
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of "votes". In case of a regression, the classifier response is the average of the responses over
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all the trees in the forest.
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All the trees are trained with the same parameters but on different training sets. These sets are
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generated from the original training set using the bootstrap procedure: for each training set, you
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randomly select the same number of vectors as in the original set ( =N ). The vectors are chosen
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with replacement. That is, some vectors will occur more than once and some will be absent. At each
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node of each trained tree, not all the variables are used to find the best split, but a random
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subset of them. With each node a new subset is generated. However, its size is fixed for all the
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nodes and all the trees. It is a training parameter set to \f$\sqrt{number\_of\_variables}\f$ by
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default. None of the built trees are pruned.
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In random trees there is no need for any accuracy estimation procedures, such as cross-validation or
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bootstrap, or a separate test set to get an estimate of the training error. The error is estimated
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internally during the training. When the training set for the current tree is drawn by sampling with
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replacement, some vectors are left out (so-called _oob (out-of-bag) data_ ). The size of oob data is
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about N/3 . The classification error is estimated by using this oob-data as follows:
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- Get a prediction for each vector, which is oob relative to the i-th tree, using the very i-th
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tree.
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- After all the trees have been trained, for each vector that has ever been oob, find the
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class-<em>winner</em> for it (the class that has got the majority of votes in the trees where
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the vector was oob) and compare it to the ground-truth response.
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- Compute the classification error estimate as a ratio of the number of misclassified oob vectors
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to all the vectors in the original data. In case of regression, the oob-error is computed as the
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squared error for oob vectors difference divided by the total number of vectors.
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For the random trees usage example, please, see letter_recog.cpp sample in OpenCV distribution.
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@sa cv::ml::RTrees
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__References:__
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- _Machine Learning_, Wald I, July 2002.
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<http://stat-www.berkeley.edu/users/breiman/wald2002-1.pdf>
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- _Looking Inside the Black Box_, Wald II, July 2002.
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<http://stat-www.berkeley.edu/users/breiman/wald2002-2.pdf>
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- _Software for the Masses_, Wald III, July 2002.
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<http://stat-www.berkeley.edu/users/breiman/wald2002-3.pdf>
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- And other articles from the web site
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<http://www.stat.berkeley.edu/users/breiman/RandomForests/cc_home.htm>
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Expectation Maximization {#ml_intro_em}
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========================
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The Expectation Maximization(EM) algorithm estimates the parameters of the multivariate probability
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density function in the form of a Gaussian mixture distribution with a specified number of mixtures.
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Consider the set of the N feature vectors { \f$x_1, x_2,...,x_{N}\f$ } from a d-dimensional Euclidean
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space drawn from a Gaussian mixture:
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\f[p(x;a_k,S_k, \pi _k) = \sum _{k=1}^{m} \pi _kp_k(x), \quad \pi _k \geq 0, \quad \sum _{k=1}^{m} \pi _k=1,\f]
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\f[p_k(x)= \varphi (x;a_k,S_k)= \frac{1}{(2\pi)^{d/2}\mid{S_k}\mid^{1/2}} exp \left \{ - \frac{1}{2} (x-a_k)^TS_k^{-1}(x-a_k) \right \} ,\f]
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where \f$m\f$ is the number of mixtures, \f$p_k\f$ is the normal distribution density with the mean
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\f$a_k\f$ and covariance matrix \f$S_k\f$, \f$\pi_k\f$ is the weight of the k-th mixture. Given the
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number of mixtures \f$M\f$ and the samples \f$x_i\f$, \f$i=1..N\f$ the algorithm finds the maximum-
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likelihood estimates (MLE) of all the mixture parameters, that is, \f$a_k\f$, \f$S_k\f$ and
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\f$\pi_k\f$ :
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\f[L(x, \theta )=logp(x, \theta )= \sum _{i=1}^{N}log \left ( \sum _{k=1}^{m} \pi _kp_k(x) \right ) \to \max _{ \theta \in \Theta },\f]
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\f[\Theta = \left \{ (a_k,S_k, \pi _k): a_k \in \mathbbm{R} ^d,S_k=S_k^T>0,S_k \in \mathbbm{R} ^{d \times d}, \pi _k \geq 0, \sum _{k=1}^{m} \pi _k=1 \right \} .\f]
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The EM algorithm is an iterative procedure. Each iteration includes two steps. At the first step
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(Expectation step or E-step), you find a probability \f$p_{i,k}\f$ (denoted \f$\alpha_{i,k}\f$ in
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the formula below) of sample i to belong to mixture k using the currently available mixture
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parameter estimates:
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\f[\alpha _{ki} = \frac{\pi_k\varphi(x;a_k,S_k)}{\sum\limits_{j=1}^{m}\pi_j\varphi(x;a_j,S_j)} .\f]
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At the second step (Maximization step or M-step), the mixture parameter estimates are refined using
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the computed probabilities:
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\f[\pi _k= \frac{1}{N} \sum _{i=1}^{N} \alpha _{ki}, \quad a_k= \frac{\sum\limits_{i=1}^{N}\alpha_{ki}x_i}{\sum\limits_{i=1}^{N}\alpha_{ki}} , \quad S_k= \frac{\sum\limits_{i=1}^{N}\alpha_{ki}(x_i-a_k)(x_i-a_k)^T}{\sum\limits_{i=1}^{N}\alpha_{ki}}\f]
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Alternatively, the algorithm may start with the M-step when the initial values for \f$p_{i,k}\f$ can
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be provided. Another alternative when \f$p_{i,k}\f$ are unknown is to use a simpler clustering
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algorithm to pre-cluster the input samples and thus obtain initial \f$p_{i,k}\f$ . Often (including
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machine learning) the k-means algorithm is used for that purpose.
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One of the main problems of the EM algorithm is a large number of parameters to estimate. The
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majority of the parameters reside in covariance matrices, which are \f$d \times d\f$ elements each
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where \f$d\f$ is the feature space dimensionality. However, in many practical problems, the
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covariance matrices are close to diagonal or even to \f$\mu_k*I\f$ , where \f$I\f$ is an identity
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matrix and \f$\mu_k\f$ is a mixture-dependent "scale" parameter. So, a robust computation scheme
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could start with harder constraints on the covariance matrices and then use the estimated parameters
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as an input for a less constrained optimization problem (often a diagonal covariance matrix is
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already a good enough approximation).
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@sa cv::ml::EM
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References:
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- Bilmes98 J. A. Bilmes. _A Gentle Tutorial of the EM Algorithm and its Application to Parameter
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Estimation for Gaussian Mixture and Hidden Markov Models_. Technical Report TR-97-021,
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International Computer Science Institute and Computer Science Division, University of California
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at Berkeley, April 1998.
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Neural Networks {#ml_intro_ann}
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===============
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ML implements feed-forward artificial neural networks or, more particularly, multi-layer perceptrons
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(MLP), the most commonly used type of neural networks. MLP consists of the input layer, output
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layer, and one or more hidden layers. Each layer of MLP includes one or more neurons directionally
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linked with the neurons from the previous and the next layer. The example below represents a 3-layer
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perceptron with three inputs, two outputs, and the hidden layer including five neurons:
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All the neurons in MLP are similar. Each of them has several input links (it takes the output values
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from several neurons in the previous layer as input) and several output links (it passes the
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response to several neurons in the next layer). The values retrieved from the previous layer are
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summed up with certain weights, individual for each neuron, plus the bias term. The sum is
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transformed using the activation function \f$f\f$ that may be also different for different neurons.
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In other words, given the outputs \f$x_j\f$ of the layer \f$n\f$ , the outputs \f$y_i\f$ of the
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layer \f$n+1\f$ are computed as:
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\f[u_i = \sum _j (w^{n+1}_{i,j}*x_j) + w^{n+1}_{i,bias}\f]
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\f[y_i = f(u_i)\f]
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Different activation functions may be used. ML implements three standard functions:
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- Identity function ( cv::ml::ANN_MLP::IDENTITY ): \f$f(x)=x\f$
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- Symmetrical sigmoid ( cv::ml::ANN_MLP::SIGMOID_SYM ): \f$f(x)=\beta*(1-e^{-\alpha
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x})/(1+e^{-\alpha x}\f$ ), which is the default choice for MLP. The standard sigmoid with
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\f$\beta =1, \alpha =1\f$ is shown below:
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- Gaussian function ( cv::ml::ANN_MLP::GAUSSIAN ): \f$f(x)=\beta e^{-\alpha x*x}\f$ , which is not
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completely supported at the moment.
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In ML, all the neurons have the same activation functions, with the same free parameters (
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\f$\alpha, \beta\f$ ) that are specified by user and are not altered by the training algorithms.
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So, the whole trained network works as follows:
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1. Take the feature vector as input. The vector size is equal to the size of the input layer.
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2. Pass values as input to the first hidden layer.
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3. Compute outputs of the hidden layer using the weights and the activation functions.
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4. Pass outputs further downstream until you compute the output layer.
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So, to compute the network, you need to know all the weights \f$w^{n+1)}_{i,j}\f$ . The weights are
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computed by the training algorithm. The algorithm takes a training set, multiple input vectors with
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the corresponding output vectors, and iteratively adjusts the weights to enable the network to give
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the desired response to the provided input vectors.
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The larger the network size (the number of hidden layers and their sizes) is, the more the potential
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network flexibility is. The error on the training set could be made arbitrarily small. But at the
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same time the learned network also "learns" the noise present in the training set, so the error on
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the test set usually starts increasing after the network size reaches a limit. Besides, the larger
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networks are trained much longer than the smaller ones, so it is reasonable to pre-process the data,
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using cv::PCA or similar technique, and train a smaller network on only essential features.
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Another MLP feature is an inability to handle categorical data as is. However, there is a
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workaround. If a certain feature in the input or output (in case of n -class classifier for
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\f$n>2\f$ ) layer is categorical and can take \f$M>2\f$ different values, it makes sense to
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represent it as a binary tuple of M elements, where the i -th element is 1 if and only if the
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feature is equal to the i -th value out of M possible. It increases the size of the input/output
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layer but speeds up the training algorithm convergence and at the same time enables "fuzzy" values
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of such variables, that is, a tuple of probabilities instead of a fixed value.
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ML implements two algorithms for training MLP's. The first algorithm is a classical random
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sequential back-propagation algorithm. The second (default) one is a batch RPROP algorithm.
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@sa cv::ml::ANN_MLP
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Logistic Regression {#ml_intro_lr}
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===================
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ML implements logistic regression, which is a probabilistic classification technique. Logistic
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Regression is a binary classification algorithm which is closely related to Support Vector Machines
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(SVM). Like SVM, Logistic Regression can be extended to work on multi-class classification problems
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like digit recognition (i.e. recognizing digits like 0,1 2, 3,... from the given images). This
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version of Logistic Regression supports both binary and multi-class classifications (for multi-class
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it creates a multiple 2-class classifiers). In order to train the logistic regression classifier,
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Batch Gradient Descent and Mini-Batch Gradient Descent algorithms are used (see
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<http://en.wikipedia.org/wiki/Gradient_descent_optimization>). Logistic Regression is a
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discriminative classifier (see <http://www.cs.cmu.edu/~tom/NewChapters.html> for more details).
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Logistic Regression is implemented as a C++ class in LogisticRegression.
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In Logistic Regression, we try to optimize the training parameter \f$\theta\f$ such that the
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hypothesis \f$0 \leq h_\theta(x) \leq 1\f$ is achieved. We have \f$h_\theta(x) = g(h_\theta(x))\f$
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and \f$g(z) = \frac{1}{1+e^{-z}}\f$ as the logistic or sigmoid function. The term "Logistic" in
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Logistic Regression refers to this function. For given data of a binary classification problem of
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classes 0 and 1, one can determine that the given data instance belongs to class 1 if \f$h_\theta(x)
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\geq 0.5\f$ or class 0 if \f$h_\theta(x) < 0.5\f$ .
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In Logistic Regression, choosing the right parameters is of utmost importance for reducing the
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training error and ensuring high training accuracy:
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- The learning rate can be set with @ref cv::ml::LogisticRegression::setLearningRate "setLearningRate"
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|
method. It determines how fast we approach the solution. It is a positive real number.
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- Optimization algorithms like Batch Gradient Descent and Mini-Batch Gradient Descent are supported
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in LogisticRegression. It is important that we mention the number of iterations these optimization
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algorithms have to run. The number of iterations can be set with @ref
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cv::ml::LogisticRegression::setIterations "setIterations". This parameter can be thought
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as number of steps taken and learning rate specifies if it is a long step or a short step. This
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and previous parameter define how fast we arrive at a possible solution.
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- In order to compensate for overfitting regularization is performed, which can be enabled with
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@ref cv::ml::LogisticRegression::setRegularization "setRegularization". One can specify what
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|
kind of regularization has to be performed by passing one of @ref
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cv::ml::LogisticRegression::RegKinds "regularization kinds" to this method.
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|
- Logistic regression implementation provides a choice of 2 training methods with Batch Gradient
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|
Descent or the MiniBatch Gradient Descent. To specify this, call @ref
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|
cv::ml::LogisticRegression::setTrainMethod "setTrainMethod" with either @ref
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|
cv::ml::LogisticRegression::BATCH "LogisticRegression::BATCH" or @ref
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|
cv::ml::LogisticRegression::MINI_BATCH "LogisticRegression::MINI_BATCH". If training method is
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|
set to @ref cv::ml::LogisticRegression::MINI_BATCH "MINI_BATCH", the size of the mini batch has
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|
to be to a positive integer set with @ref cv::ml::LogisticRegression::setMiniBatchSize
|
|
"setMiniBatchSize".
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A sample set of training parameters for the Logistic Regression classifier can be initialized as follows:
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|
@snippet samples/cpp/logistic_regression.cpp init
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|
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|
@sa cv::ml::LogisticRegression
|