LinearRegressionNewtonMethod Class

Initializes a new instance of the LinearRegressionNewtonMethod class.

Obsolete.

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.

Insensitivity zone ε. Increasing the value of ε can result in fewer support vectors in the created model. Default value is 1e-3.

Gets or sets the input vectors for training.

Gets whether the machine to be learned has a Linear kernel.

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 the machine to be taught.

Gets or sets the output values for each calibration vector.

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

Convergence tolerance. Default value is 0.01.

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

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 individual weight of each sample in the training set. If set to null, all samples will be assumed equal weight. Default is null.

Obsolete.

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 learning algorithm.

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

Creates a shallow copy of the current Object.

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.