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SVM::C_SVC
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The basic C_SVC SVM type. The default, and a good starting point
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SVM::NU_SVC
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The NU_SVC type uses a different, more flexible, error weighting
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SVM::ONE_CLASS
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One class SVM type. Train just on a single class, using outliers as negative examples
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SVM::EPSILON_SVR
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A SVM type for regression (predicting a value rather than just a class)
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SVM::NU_SVR
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A NU style SVM regression type
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SVM::KERNEL_LINEAR
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A very simple kernel, can work well on large document classification problems
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SVM::KERNEL_POLY
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A polynomial kernel
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SVM::KERNEL_RBF
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The common Gaussian RBD kernel. Handles non-linear problems well and is a good default for classification
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SVM::KERNEL_SIGMOID
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A kernel based on the sigmoid function. Using this makes the SVM very similar to a two layer sigmoid based neural network
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SVM::KERNEL_PRECOMPUTED
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A precomputed kernel - currently unsupported.
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SVM::OPT_TYPE
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The options key for the SVM type
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SVM::OPT_KERNEL_TYPE
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The options key for the kernel type
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SVM::OPT_DEGREE
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SVM::OPT_SHRINKING
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Training parameter, boolean, for whether to use the shrinking heuristics
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SVM::OPT_PROBABILITY
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Training parameter, boolean, for whether to collect and use probability estimates
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SVM::OPT_GAMMA
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Algorithm parameter for Poly, RBF and Sigmoid kernel types.
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SVM::OPT_NU
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The option key for the nu parameter, only used in the NU_ SVM types
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SVM::OPT_EPS
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The option key for the Epsilon parameter, used in epsilon regression
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SVM::OPT_P
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Training parameter used by Episilon SVR regression
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SVM::OPT_COEF_ZERO
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Algorithm parameter for poly and sigmoid kernels
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SVM::OPT_C
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The option for the cost parameter that controls tradeoff between errors and generality - effectively the penalty for misclassifying training examples.
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SVM::OPT_CACHE_SIZE
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Memory cache size, in MB