LeastSquaresLearning Class

Initializes a new instance of the LeastSquaresLearning class.

Obsolete.

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

Gets or sets the cache size to partially stored the kernel matrix. Default is the same number of input vectors.

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 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 1e-6.

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.

Computes the error rate for a given set of input and outputs.

Creates an instance of the model to be learned. Inheritors of this abstract class must define this method so new models can be created from the training data.

Determines whether the specified object is equal to the current object.

Allows an object to try to free resources and perform other cleanup operations before it is reclaimed by garbage collection.

Serves as the default hash function.

Gets the Type of the current instance.

Runs the main body of the learning algorithm.

Learns a model that can map the given inputs to the given outputs.

Learns a model that can map the given inputs to the given outputs.

Learns a model that can map the given inputs to the given outputs.

Learns a model that can map the given inputs to the given outputs.

Learns a model that can map the given inputs to the given outputs.

Creates a shallow copy of the current Object.

Obsolete.

Obsolete.

Returns a string that represents the current object.

Checks whether an object implements a method with the given name.

Compares two objects for equality, performing an elementwise comparison if the elements are vectors or matrices.

Converts an object into another type, irrespective of whether the conversion can be done at compile time or not. This can be used to convert generic types to numeric types during runtime.

Converts an object into another type, irrespective of whether the conversion can be done at compile time or not. This can be used to convert generic types to numeric types during runtime.