LinearNewtonMethod Class

L2-regularized L2-loss linear support vector classification (primal).

Inheritance Hierarchy

SystemObject
  Accord.MachineLearningBinaryLearningBaseSupportVectorMachine, Double
    Accord.MachineLearning.VectorMachines.LearningBaseSupportVectorClassificationSupportVectorMachine, Linear, Double
      Accord.MachineLearning.VectorMachines.LearningBaseLinearNewtonMethodSupportVectorMachine, Linear, Double
        Accord.MachineLearning.VectorMachines.LearningBaseLinearNewtonMethodSupportVectorMachine, Linear
          Accord.MachineLearning.VectorMachines.LearningLinearNewtonMethod

Namespace: Accord.MachineLearning.VectorMachines.Learning
Assembly: Accord.MachineLearning (in Accord.MachineLearning.dll) Version: 3.8.0

Syntax

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public class LinearNewtonMethod : BaseLinearNewtonMethod<SupportVectorMachine, Linear>, 
	ILinearSupportVectorMachineLearning, ISupervisedLearning<SupportVectorMachine, double[], double>, 
	ISupervisedLearning<SupportVectorMachine, double[], int>, ISupervisedLearning<SupportVectorMachine, double[], bool>, 
	ISupportVectorMachineLearning, ISupportVectorMachineLearning<double[]>, ISupervisedBinaryLearning<ISupportVectorMachine<double[]>, double[]>, 
	ISupervisedMulticlassLearning<ISupportVectorMachine<double[]>, double[]>, ISupervisedMultilabelLearning<ISupportVectorMachine<double[]>, double[]>, 
	ISupervisedLearning<ISupportVectorMachine<double[]>, double[], int[]>, 
	ISupervisedLearning<ISupportVectorMachine<double[]>, double[], bool[]>, 
	ISupervisedLearning<ISupportVectorMachine<double[]>, double[], int>, 
	ISupervisedLearning<ISupportVectorMachine<double[]>, double[], bool>, 
	ISupervisedLearning<ISupportVectorMachine<double[]>, double[], double>

Public Class LinearNewtonMethod
	Inherits BaseLinearNewtonMethod(Of SupportVectorMachine, Linear)
	Implements ILinearSupportVectorMachineLearning, ISupervisedLearning(Of SupportVectorMachine, Double(), Double), 
	ISupervisedLearning(Of SupportVectorMachine, Double(), Integer), ISupervisedLearning(Of SupportVectorMachine, Double(), Boolean), 
	ISupportVectorMachineLearning, ISupportVectorMachineLearning(Of Double()), ISupervisedBinaryLearning(Of ISupportVectorMachine(Of Double()), Double()), 
	ISupervisedMulticlassLearning(Of ISupportVectorMachine(Of Double()), Double()), 
	ISupervisedMultilabelLearning(Of ISupportVectorMachine(Of Double()), Double()), 
	ISupervisedLearning(Of ISupportVectorMachine(Of Double()), Double(), Integer()), 
	ISupervisedLearning(Of ISupportVectorMachine(Of Double()), Double(), Boolean()), 
	ISupervisedLearning(Of ISupportVectorMachine(Of Double()), Double(), Integer), 
	ISupervisedLearning(Of ISupportVectorMachine(Of Double()), Double(), Boolean), 
	ISupervisedLearning(Of ISupportVectorMachine(Of Double()), Double(), Double)

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The LinearNewtonMethod type exposes the following members.

Constructors

	Name	Description
	LinearNewtonMethod	Initializes a new instance of the LinearNewtonMethod class.
	LinearNewtonMethod(KernelSupportVectorMachine, Double, Int32)	Obsolete. Obsolete.
	LinearNewtonMethod(SupportVectorMachine, Double, Int32)	Obsolete. Obsolete.

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Properties

	Name	Description
	C	Gets or sets the cost values associated with each input vector. (Inherited from BaseSupportVectorClassificationTModel, TKernel, TInput.)
	Complexity	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. (Inherited from BaseSupportVectorClassificationTModel, TKernel, TInput.)
	Inputs	Gets or sets the input vectors for training. (Inherited from BaseSupportVectorClassificationTModel, TKernel, TInput.)
	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. (Inherited from BaseSupportVectorClassificationTModel, TKernel, TInput.)
	MaximumIterations	Gets or sets the maximum number of iterations that should be performed until the algorithm stops. Default is 1000. (Inherited from BaseLinearNewtonMethodTModel, TKernel, TInput.)
	Model	Gets or sets the classifier being learned. (Inherited from BinaryLearningBaseTModel, TInput.)
	NegativeWeight	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. (Inherited from BaseSupportVectorClassificationTModel, TKernel, TInput.)
	Outputs	Gets or sets the output labels for each training vector. (Inherited from BaseSupportVectorClassificationTModel, TKernel, TInput.)
	PositiveWeight	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. (Inherited from BaseSupportVectorClassificationTModel, TKernel, TInput.)
	Token	Gets or sets a cancellation token that can be used to stop the learning algorithm while it is running. (Inherited from BinaryLearningBaseTModel, TInput.)
	Tolerance	Convergence tolerance. Default value is 0.1. (Inherited from BaseLinearNewtonMethodTModel, TKernel, TInput.)
	UseClassProportions	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. (Inherited from BaseSupportVectorClassificationTModel, TKernel, TInput.)
	UseComplexityHeuristic	Gets or sets a value indicating whether the Complexity parameter C should be computed automatically by employing an heuristic rule. Default is true. (Inherited from BaseSupportVectorClassificationTModel, TKernel, TInput.)
	UseKernelEstimation	Gets or sets whether initial values for some kernel parameters should be estimated from the data, if possible. Default is true. (Inherited from BaseSupportVectorClassificationTModel, TKernel, TInput.)
	WeightRatio	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. (Inherited from BaseSupportVectorClassificationTModel, TKernel, TInput.)

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Methods

	Name	Description
	ComputeError	Obsolete. Computes the error rate for a given set of input and outputs. (Inherited from BaseSupportVectorClassificationTModel, TKernel, TInput.)
	Create	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. (Overrides BaseSupportVectorClassificationTModel, TKernel, TInputCreate(Int32, TKernel).)
	Equals	Determines whether the specified object is equal to the current object. (Inherited from Object.)
	Finalize	Allows an object to try to free resources and perform other cleanup operations before it is reclaimed by garbage collection. (Inherited from Object.)
	GetHashCode	Serves as the default hash function. (Inherited from Object.)
	GetType	Gets the Type of the current instance. (Inherited from Object.)
	InnerRun	Runs the learning algorithm. (Inherited from BaseLinearNewtonMethodTModel, TKernel, TInput.)
	Learn(TInput, Boolean, Double)	Learns a model that can map the given inputs to the given outputs. (Inherited from BinaryLearningBaseTModel, TInput.)
	Learn(TInput, Double, Double)	Learns a model that can map the given inputs to the given outputs. (Inherited from BinaryLearningBaseTModel, TInput.)
	Learn(TInput, Int32, Double)	Learns a model that can map the given inputs to the given outputs. (Inherited from BinaryLearningBaseTModel, TInput.)
	Learn(TInput, Int32, Double)	Learns a model that can map the given inputs to the given outputs. (Inherited from BinaryLearningBaseTModel, TInput.)
	Learn(TInput, Boolean, Double)	Learns a model that can map the given inputs to the given outputs. (Inherited from BaseSupportVectorClassificationTModel, TKernel, TInput.)
	MemberwiseClone	Creates a shallow copy of the current Object. (Inherited from Object.)
	Run	Obsolete. Obsolete. (Inherited from BaseSupportVectorClassificationTModel, TKernel, TInput.)
	Run(Boolean)	Obsolete. Obsolete. (Inherited from BaseSupportVectorClassificationTModel, TKernel, TInput.)
	ToString	Returns a string that represents the current object. (Inherited from Object.)

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Extension Methods

	Name	Description
	HasMethod	Checks whether an object implements a method with the given name. (Defined by ExtensionMethods.)
	IsEqual	Compares two objects for equality, performing an elementwise comparison if the elements are vectors or matrices. (Defined by Matrix.)
	To(Type)	Overloaded. 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. (Defined by ExtensionMethods.)
	ToT	Overloaded. 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. (Defined by ExtensionMethods.)

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Remarks

This class implements a L2-regularized L2-loss support vector machine learning algorithm that operates in the primal form of the optimization problem. This method has been based on liblinear's l2r_l2_svc_fun problem specification, optimized using a Trust-region Newton method. This method might be faster than the often preferred LinearDualCoordinateDescent.

Liblinear's solver -s 2: L2R_L2LOSS_SVC. A trust region newton algorithm for the primal of L2-regularized, L2-loss linear support vector classification.

Reference

Accord.MachineLearning.VectorMachines.Learning Namespace

Accord.MachineLearning.VectorMachines.LearningSequentialMinimalOptimizationTKernel

Accord.MachineLearning.VectorMachines.LearningLinearDualCoordinateDescent