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Support Vector Machine Classifier.
This is a simple interface to the libSVM package.
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probabilities = StateVariable(enabled= False, doc= "Estimates
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_KERNELS = {"linear":(svm.svmc.LINEAR, None, LinearSVMWeights)
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_KNOWN_PARAMS = ['epsilon', 'probability', 'shrinking', 'weigh
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_KNOWN_KERNEL_PARAMS = ['cache_size']
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_KNOWN_IMPLEMENTATIONS = {'C_SVC':(svm.svmc.C_SVC, ('C',), ('b
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_clf_internals = _SVM._clf_internals+ ['libsvm'] Describes some specifics about the classifier -- is that it is doing regression for instance.... |
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model = property(fget= lambda self: self.__model) Access to the SVM model. |
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__model Holds the trained SVM. |
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This is the base class of all classifier that utilize the libSVM package underneath. It is not really meant to be used directly. Unless you know what you are doing it is most likely better to use one of the subclasses. Here is the explaination for some of the parameters from the libSVM documentation: svm_type can be one of C_SVC, NU_SVC, ONE_CLASS, EPSILON_SVR, NU_SVR.
kernel_type can be one of LINEAR, POLY, RBF, SIGMOID.
cache_size is the size of the kernel cache, specified in megabytes. C is the cost of constraints violation. (we usually use 1 to 1000) eps is the stopping criterion. (we usually use 0.00001 in nu-SVC, 0.001 in others). nu is the parameter in nu-SVM, nu-SVR, and one-class-SVM. p is the epsilon in epsilon-insensitive loss function of epsilon-SVM regression. shrinking = 1 means shrinking is conducted; = 0 otherwise. probability = 1 means model with probability information is obtained; = 0 otherwise. nr_weight, weight_label, and weight are used to change the penalty for some classes (If the weight for a class is not changed, it is set to 1). This is useful for training classifier using unbalanced input data or with asymmetric misclassification cost. Each weight[i] corresponds to weight_label[i], meaning that the penalty of class weight_label[i] is scaled by a factor of weight[i]. If you do not want to change penalty for any of the classes, just set nr_weight to 0.
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probabilities
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_KERNELS
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_KNOWN_PARAMS
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_KNOWN_IMPLEMENTATIONS
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