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SVMClassificationTrainNuSVC Method

Train \nu - Support Vector Classifier

Namespace:  FinMath.MachineLearning.SVM
Assembly:  FinMath (in FinMath.dll) Version: 2.4.7-0a995bd0ea1854c2c868ec3f8dae606c5777e170
Syntax
C#
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public void TrainNuSVC(
	Matrix observations,
	IntegerArray classes,
	bool autoScale = true,
	SVMKernelBase kernel = null,
	double nu = 0.5,
	double cacheSize = 100,
	double eps = 0.001,
	int maximumIterations = 0,
	TimeSpan modelTrainTimeout = null,
	bool shrinkingHeuristics = true
)

Parameters

observations
Type: FinMath.LinearAlgebraMatrix
Observation matrix (N*M size). Columns corresponds to factors, rows to observation.
classes
Type: FinMath.DataStructuresIntegerArray
The vector (N size) class indexes.
autoScale (Optional)
Type: SystemBoolean
Auto scale observations. Normalize the observations matrix such that columns of observations are centered to have mean 0 and scaled to have standard deviation 1.
kernel (Optional)
Type: FinMath.MachineLearning.SVMSVMKernelBase
The kernel function for SVM model (default RadialBasisFunction).
nu (Optional)
Type: SystemDouble
The \nu value. Its should be between 0.0 and 0.5 (default 0.5).
cacheSize (Optional)
Type: SystemDouble
The cache memory size in MB (default 100).
eps (Optional)
Type: SystemDouble
The tolerance of termination criterion (default 0.001).
maximumIterations (Optional)
Type: SystemInt32
The maximum iterations number (0 means auto).
modelTrainTimeout (Optional)
Type: SystemTimeSpan
One model train timeout (0 means no restriction).
shrinkingHeuristics (Optional)
Type: SystemBoolean
Whether to use the shrinking heuristics flag (default true).
See Also