create_class_svm — Create a support vector machine for pattern classification.
create_class_svm creates a support vector machine that can be used for pattern classification. The dimension of the patterns to be classified is specified in NumFeatures, the number of different classes in NumClasses.
For a binary classification problem in which the classes are linearly separable the SVM algorithm selects data vectors from the training set that are utilized to construct the optimal separating hyperplane between different classes. This hyperplane is optimal in the sense that the margin between the convex hulls of the different classes is maximized. The training patterns that are located at the margin define the hyperplane and are called support vectors (SV).
Classification of a feature vector z is performed with the following formula:
For classification problems in which the classes are not linearly separable the algorithm is extended in two ways. First, during training a certain amount of errors (overlaps) is compensated with the use of slack variables. This means that the are upper bounded by a regularization constant. To enable an intuitive control of the amount of training errors, the Nu-SVM version of the training algorithm is used. Here, the regularization parameter Nu is an asymptotic upper bound on the number of training errors and an asymptotic lower bound on the number of support vectors. As a rule of thumb, the parameter Nu should be set to the prior expectation of the application's specific error ratio, e.g., 0.01 (corresponding to a maximum training error of 1%). Please note that a too big value for Nu might lead to an infeasible training problem, i.e., the SVM cannot be trained correctly (see train_class_svm for more details). Since this can only be determined during training, an exception can only be raised there. In this case, a new SVM with Nu chosen smaller must be created.
Second, because the above SVM exclusively calculates dot products between the feature vectors, it is possible to incorporate a kernel function into the training and testing algorithm. This means that the dot products are substituted by a kernel function, which implicitly performs the dot product in a higher dimensional feature space. Given the appropriate kernel transformation, an originally not linearly separable classification task becomes linearly separable in the higher dimensional feature space.
Different kernel functions can be selected with the parameter KernelType. For KernelType = 'linear' the dot product, as specified in the above formula is calculated. This kernel should solely be used for linearly or nearly linearly separable classification tasks. The parameter KernelParam is ignored here.
The radial basis function (RBF) KernelType = 'rbf' is the best choice for a kernel function because it achieves good results for many classification tasks. It is defined as:
With KernelType = 'polynomial_homogeneous' or 'polynomial_inhomogeneous', polynomial kernels can be selected. They are defined in the following way:
As a rule of thumb, the RBF kernel provides a good choice for most of the classification problems and should therefore be used in almost all cases. Nevertheless, the linear and polynomial kernels might be better suited for certain applications and can be tested for comparison. Please note that the novelty-detection Mode and the operator reduce_class_svm are provided only for the RBF kernel.
Mode specifies the general classification task, which is either how to break down a multi-class decision problem to binary sub-cases or whether to use a special classifier mode called 'novelty-detection'. Mode = 'one-versus-all' creates a classifier where each class is compared to the rest of the training data. During testing the class with the largest output (see the classification formula without sign) is chosen. Mode = 'one-versus-one' creates a binary classifier between each single class. During testing a vote is cast and the class with the majority of the votes is selected. The optimal Mode for multi-class classification depends on the number of classes. Given n classes 'one-versus-all' creates n classifiers, whereas 'one-versus-one' creates n(n-1)/2. Note that for a binary decision task 'one-versus-one' would create exactly one, whereas 'one-versus-all' unnecessarily creates two symmetric classifiers. For few classes (approximately up to 10) 'one-versus-one' is faster for training and testing, because the sub-classifier all consist of fewer training data and result in overall fewer support vectors. In case of many classes 'one-versus-all' is preferable, because 'one-versus-one' generates a prohibitively large amount of sub-classifiers, as their number grows quadratically with the number of classes.
A special case of classification is Mode = 'novelty-detection', where the test data is classified only with regard to membership to the training data, i.e., NumClasses must be set to 1. The separating hyperplane lies around the training data and thereby implicitly divides the training data from the rejection class. The advantage is that the rejection class is not defined explicitly, which is difficult to do in certain applications like texture classification. The resulting support vectors are all lying at the border. With the parameter Nu, the ratio of outliers in the training data set is specified. Note, that when classifying in the 'novelty-detection' mode, the class of the training data is returned with index 1 and the rejection class is returned with index 0. Thus, the first class serves as rejection class. In contrast, when using the MLP classifier, the last class serves as rejection class by default.
The parameters Preprocessing and NumComponents can be used to specify a preprocessing of the feature vectors. For Preprocessing = 'none', the feature vectors are passed unaltered to the SVM. NumComponents is ignored in this case.
For all other values of Preprocessing, the training data set is used to compute a transformation of the feature vectors during the training as well as later in the classification.
For Preprocessing = 'normalization', the feature vectors are normalized. In case of a polynomial kernel, the minimum and maximum value of the training data set is transformed to -1 and +1. In case of the RBF kernel, the data is normalized by subtracting the mean of the training vectors and dividing the result by the standard deviation of the individual components of the training vectors. Hence, the transformed feature vectors have a mean of 0 and a standard deviation of 1. The normalization does not change the length of the feature vector. NumComponents is ignored in this case. This transformation can be used if the mean and standard deviation of the feature vectors differs substantially from 0 and 1, respectively, or for data in which the components of the feature vectors are measured in different units (e.g., if some of the data are gray value features and some are region features, or if region features are mixed, e.g., 'circularity' (unit: scalar) and 'area' (unit: pixel squared)). The normalization transformation should be performed in general, because it increases the numerical stability during training/testing.
For Preprocessing = 'principal_components', a principal component analysis (PCA) is performed. First, the feature vectors are normalized (see above). Then, an orthogonal transformation (a rotation in the feature space) that decorrelates the training vectors is computed. After the transformation, the mean of the training vectors is 0 and the covariance matrix of the training vectors is a diagonal matrix. The transformation is chosen such that the transformed features that contain the most variation is contained in the first components of the transformed feature vector. With this, it is possible to omit the transformed features in the last components of the feature vector, which typically are mainly influenced by noise, without losing a large amount of information. The parameter NumComponents can be used to determine how many of the transformed feature vector components should be used. Up to NumFeatures components can be selected. The operator get_prep_info_class_svm can be used to determine how much information each transformed component contains. Hence, it aids the selection of NumComponents. Like data normalization, this transformation can be used if the mean and standard deviation of the feature vectors differs substantially from 0 and 1, respectively, or for feature vectors in which the components of the data are measured in different units. In addition, this transformation is useful if it can be expected that the features are highly correlated. Please note that the RBF kernel is very robust against the dimensionality reduction performed by PCA and should therefore be the first choice when speeding up the classification time.
The transformation specified by Preprocessing = 'canonical_variates' first normalizes the training vectors and then decorrelates the training vectors on average over all classes. At the same time, the transformation maximally separates the mean values of the individual classes. As for Preprocessing = 'principal_components', the transformed components are sorted by information content, and hence transformed components with little information content can be omitted. For canonical variates, up to min(NumClasses-1, NumFeatures) components can be selected. Also in this case, the information content of the transformed components can be determined with get_prep_info_class_svm. Like principal component analysis, canonical variates can be used to reduce the amount of data without losing a large amount of information, while additionally optimizing the separability of the classes after the data reduction. The computation of the canonical variates is also called linear discriminant analysis.
For the last two types of transformations ('principal_components' and 'canonical_variates'), the length of input data of the SVM is determined by NumComponents, whereas NumFeatures determines the dimensionality of the input data (i.e., the length of the untransformed feature vector). Hence, by using one of these two transformations, the size of the SVM with respect to data length is reduced, leading to shorter training/classification times by the SVM.
After the SVM has been created with create_class_svm, typically training samples are added to the SVM by repeatedly calling add_sample_class_svm or read_samples_class_svm. After this, the SVM is typically trained using train_class_svm. Hereafter, the SVM can be saved using write_class_svm. Alternatively, the SVM can be used immediately after training to classify data using classify_class_svm.
A comparison of the SVM and the multi-layer perceptron (MLP) (see create_class_mlp) typically shows that SVMs are generally faster at training, especially for huge training sets, and achieve slightly better recognition rates than MLPs. The MLP is faster at classification and should therefore be preferred in time critical applications. Please note that this guideline assumes optimal tuning of the parameters.
This operator returns a handle. Note that the state of an instance of this handle type may be changed by specific operators even though the handle is used as an input parameter by those operators.
Number of input variables (features) of the SVM.
Default value: 10
Suggested values: 1, 2, 3, 4, 5, 8, 10, 15, 20, 30, 40, 50, 60, 70, 80, 90, 100
Restriction: NumFeatures >= 1
The kernel type.
Default value: 'rbf'
List of values: 'linear', 'polynomial_homogeneous', 'polynomial_inhomogeneous', 'rbf'
Additional parameter for the kernel function. In case of RBF kernel the value for . For polynomial kernel the degree
Default value: 0.02
Suggested values: 0.01, 0.02, 0.05, 0.1, 0.5
Regularisation constant of the SVM.
Default value: 0.05
Suggested values: 0.0001, 0.001, 0.01, 0.05, 0.1, 0.2, 0.3
Restriction: Nu > 0.0 && Nu < 1.0
Number of classes.
Default value: 5
Suggested values: 2, 3, 4, 5, 6, 7, 8, 9, 10
Restriction: NumClasses >= 1
The mode of the SVM.
Default value: 'one-versus-one'
List of values: 'novelty-detection', 'one-versus-all', 'one-versus-one'
Type of preprocessing used to transform the feature vectors.
Default value: 'normalization'
List of values: 'canonical_variates', 'none', 'normalization', 'principal_components'
Default value: 10
Suggested values: 1, 2, 3, 4, 5, 8, 10, 15, 20, 30, 40, 50, 60, 70, 80, 90, 100
Restriction: NumComponents >= 1
create_class_svm (NumFeatures, 'rbf', 0.01, 0.01, NumClasses,\ 'one-versus-all', 'normalization', NumFeatures,\ SVMHandle) * Generate and add the training data for J := 0 to NumData-1 by 1 * Generate training features and classes * Data = [...] * Class = ... add_sample_class_svm (SVMHandle, Data, Class) endfor * Train the SVM train_class_svm (SVMHandle, 0.001, 'default') * Use the SVM to classify unknown data for J := 0 to N-1 by 1 * Extract features * Features = [...] classify_class_svm (SVMHandle, Features, 1, Class) endfor clear_class_svm (SVMHandle)
If the parameters are valid the operator create_class_svm returns the value 2 (H_MSG_TRUE). If necessary, an exception is raised.
create_class_mlp, create_class_gmm, create_class_box
clear_class_svm, train_class_svm, classify_class_svm
Bernhard Schölkopf, Alexander J.Smola: “Learning with Kernels”;
MIT Press, London; 1999.
John Shawe-Taylor, Nello Cristianini: “Kernel Methods for Pattern Analysis”; Cambridge University Press, Cambridge; 2004.