reduce_class_svm — Approximate a trained support vector machine by a reduced support
vector machine for faster classification.
As described in
create_class_svm, the classification time of
a SVM depends on the number of kernel evaluations between the
support vectors and the feature vectors. While the length of the
data vectors can be reduced in a preprocessing step like
'principal_components' or 'canonical_variates' (see
create_class_svm for details), the number of resulting SV
depends on the complexity of the classification problem. The number
of SVs is determined during training. To further reduce
classification time, the number of SVs can be reduced by
approximating the original separating hyperplane with fewer SVs than
originally required. For this purpose, a copy of the original SVM
SVMHandle is created and returned in
SVMHandleReduced. This new SVM has the same
parametrization as the original SVM, but a different SV expansion.
The training samples that are included in
SVMHandle are not
copied. The original SVM is not modified by
The reduction method is selected with
only a bottom up approach is supported, which iteratively merges SVs.
The algorithm stops if either the minimum number of SVs is reached
MinRemainingSV) or if the accumulated maximum error exceeds
MaxError. Note that the approximation reduces
the complexity of the hyperplane and thereby leads to a deteriorated
classification rate. A common approach is therefore to start from a
MaxError e.g., 0.001, and to increase its
value step by step. To control the reduction ratio, at each step
the number of remaining SVs is determined with
get_support_vector_num_class_svm and the classification rate
is checked on a separate test data set with
Original SVM handle.
Type of postprocessing to reduce number of SV.
Default value: 'bottom_up'
List of values: 'bottom_up'
Minimum number of remaining SVs.
Default value: 2
Suggested values: 2, 3, 4, 5, 7, 10, 15, 20, 30, 50
MinRemainingSV >= 2
Maximum allowed error of reduction.
Default value: 0.001
Suggested values: 0.0001, 0.0002, 0.0005, 0.001, 0.002, 0.005, 0.01, 0.02, 0.05
MaxError > 0.0
SVMHandle of reduced SVM.
* Train an SVM create_class_svm (NumFeatures, 'rbf', 0.01, 0.01, NumClasses,\ 'one-versus-all', 'normalization', NumFeatures,\ SVMHandle) read_samples_class_svm (SVMHandle, 'samples.mtf') train_class_svm (SVMHandle, 0.001, 'default') * Create a reduced SVM reduce_class_svm (SVMHandle, 'bottom_up', 2, 0.01, SVMHandleReduced) write_class_svm (SVMHandleReduced, 'classifier.svm')
If the parameters are valid the operator
returns the value TRUE. If necessary, an exception is