LinearNewtonMethod Properties

Gets or sets the cost values associated with each input vector.

Complexity (cost) parameter C. Increasing the value of C forces the creation of a more accurate model that may not generalize well. If this value is not set and UseComplexityHeuristic is set to true, the framework will automatically guess a value for C. If this value is manually set to something else, then UseComplexityHeuristic will be automatically disabled and the given value will be used instead.

Gets or sets the input vectors for training.

Gets or sets the kernel function use to create a kernel Support Vector Machine. If this property is set, UseKernelEstimation will be set to false.

Gets or sets the maximum number of iterations that should be performed until the algorithm stops. Default is 1000.

Gets or sets the classifier being learned.

Gets or sets the negative class weight. This should be a value higher than 0 indicating how much of the Complexity parameter C should be applied to instances carrying the negative label.

Gets or sets the output labels for each training vector.

Gets or sets the positive class weight. This should be a value higher than 0 indicating how much of the Complexity parameter C should be applied to instances carrying the positive label.

Gets or sets a cancellation token that can be used to stop the learning algorithm while it is running.

Convergence tolerance. Default value is 0.1.

Gets or sets a value indicating whether the weight ratio to be used between Complexity values for negative and positive instances should be computed automatically from the data proportions. Default is false.

Gets or sets a value indicating whether the Complexity parameter C should be computed automatically by employing an heuristic rule. Default is true.

Gets or sets whether initial values for some kernel parameters should be estimated from the data, if possible. Default is true.

Gets or sets the weight ratio between positive and negative class weights. This ratio controls how much of the Complexity parameter C should be applied to the positive class.