reduce_class_svm — 通过缩小支持向量机来近似训练好的支持向量机，以实现更快的分类。
reduce_class_svm( : : SVMHandle, Method, MinRemainingSV, MaxError : SVMHandleReduced)
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
provided by 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
reduce_class_svm.
The reduction method is selected with Method. Currently,
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
the threshold 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
small 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
classify_class_svm。
SVMHandle (输入控制) class_svm → (handle)
Original SVM handle.
Method (输入控制) string → (string)
Type of postprocessing to reduce number of SV.
默认值： 'bottom_up'
值列表： 'bottom_up'
MinRemainingSV (输入控制) integer → (integer)
Minimum number of remaining SVs.
默认值： 2
建议值： 2, 3, 4, 5, 7, 10, 15, 20, 30, 50
限制：
MinRemainingSV >= 2
MaxError (输入控制) real → (real)
Maximum allowed error of reduction.
默认值： 0.001
建议值： 0.0001, 0.0002, 0.0005, 0.001, 0.002, 0.005, 0.01, 0.02, 0.05
限制：
MaxError > 0.0
SVMHandleReduced (输出控制) class_svm → (handle)
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 train_class_svm 返回值 2 ( H_MSG_TRUE )。如有必要，则抛出异常。
train_class_svm,
get_support_vector_num_class_svm
classify_class_svm,
write_class_svm,
get_support_vector_num_class_svm
基础