SequentialMinimalOptimizationTKernel Properties

Gets the indices of the active examples (examples which have the corresponding Lagrange multiplier different than zero).

Gets the indices of the examples at the boundary (examples which have the corresponding Lagrange multipliers equal to C).

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

Gets or sets the cache size to partially store the kernel matrix. Default is the same number of input vectors, meaning the entire kernel matrix will be computed and cached in memory. If set to zero, the cache will be disabled and all operations will be computed as needed.

Gets or sets whether to produce compact models. Compact formulation is currently limited to linear models.

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.

Epsilon for round-off errors. Default value is 1e-6.

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 the value for the Lagrange multipliers (alpha) for every observation vector.

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 the indices of the non-bounded examples (examples which have the corresponding Lagrange multipliers between 0 and C).

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 value indicating whether shrinking heuristics should be used. Default is false. Note: this property can only be used when Strategy is set to SecondOrder.

Gets or sets the pair selection strategy to be used during optimization.

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

Convergence tolerance. Default value is 1e-2.

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.