BaseSequentialMinimalOptimizationTModel, TKernel, TInput Class

Base class for Sequential Minimal Optimization.

Inheritance Hierarchy

SystemObject
  Accord.MachineLearningBinaryLearningBaseTModel, TInput
    Accord.MachineLearning.VectorMachines.LearningBaseSupportVectorClassificationTModel, TKernel, TInput
      Accord.MachineLearning.VectorMachines.LearningBaseSequentialMinimalOptimizationTModel, TKernel, TInput
        Accord.MachineLearning.VectorMachines.LearningSequentialMinimalOptimization
        Accord.MachineLearning.VectorMachines.LearningSequentialMinimalOptimizationTKernel
        Accord.MachineLearning.VectorMachines.LearningSequentialMinimalOptimizationTKernel, TInput

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

Syntax

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public abstract class BaseSequentialMinimalOptimization<TModel, TKernel, TInput> : BaseSupportVectorClassification<TModel, TKernel, TInput>, 
	ISupportVectorMachineLearning<TInput>, ISupervisedBinaryLearning<ISupportVectorMachine<TInput>, TInput>, 
	ISupervisedMulticlassLearning<ISupportVectorMachine<TInput>, TInput>, ISupervisedMultilabelLearning<ISupportVectorMachine<TInput>, TInput>, 
	ISupervisedLearning<ISupportVectorMachine<TInput>, TInput, int[]>, ISupervisedLearning<ISupportVectorMachine<TInput>, TInput, bool[]>, 
	ISupervisedLearning<ISupportVectorMachine<TInput>, TInput, int>, ISupervisedLearning<ISupportVectorMachine<TInput>, TInput, bool>, 
	ISupervisedLearning<ISupportVectorMachine<TInput>, TInput, double>
where TModel : Object, ISupportVectorMachine<TInput>
where TKernel : Object, IKernel<TInput>

Public MustInherit Class BaseSequentialMinimalOptimization(Of TModel As {Object, ISupportVectorMachine(Of TInput)}, TKernel As {Object, IKernel(Of TInput)}, TInput)
	Inherits BaseSupportVectorClassification(Of TModel, TKernel, TInput)
	Implements ISupportVectorMachineLearning(Of TInput), ISupervisedBinaryLearning(Of ISupportVectorMachine(Of TInput), TInput), 
	ISupervisedMulticlassLearning(Of ISupportVectorMachine(Of TInput), TInput), ISupervisedMultilabelLearning(Of ISupportVectorMachine(Of TInput), TInput), 
	ISupervisedLearning(Of ISupportVectorMachine(Of TInput), TInput, Integer()), 
	ISupervisedLearning(Of ISupportVectorMachine(Of TInput), TInput, Boolean()), 
	ISupervisedLearning(Of ISupportVectorMachine(Of TInput), TInput, Integer), 
	ISupervisedLearning(Of ISupportVectorMachine(Of TInput), TInput, Boolean), 
	ISupervisedLearning(Of ISupportVectorMachine(Of TInput), TInput, Double)

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Type Parameters

TModel
TKernel
TInput

The BaseSequentialMinimalOptimizationTModel, TKernel, TInput type exposes the following members.

Constructors

	Name	Description
	BaseSequentialMinimalOptimizationTModel, TKernel, TInput	Initializes a new instance of the BaseSequentialMinimalOptimizationTModel, TKernel, TInput class.
	BaseSequentialMinimalOptimizationTModel, TKernel, TInput(ISupportVectorMachineTInput, TInput, Int32)	Obsolete.

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Properties

	Name	Description
	ActiveExamples	Gets the indices of the active examples (examples which have the corresponding Lagrange multiplier different than zero).
	BoundedExamples	Gets the indices of the examples at the boundary (examples which have the corresponding Lagrange multipliers equal to C).
	C	Gets or sets the cost values associated with each input vector. (Inherited from BaseSupportVectorClassificationTModel, TKernel, TInput.)
	CacheSize	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.
	Compact	Obsolete. Gets or sets whether to produce compact models. Compact formulation is currently limited to linear models.
	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.)
	Epsilon	Epsilon for round-off errors. Default value is 1e-6.
	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.)
	Lagrange	Gets the value for the Lagrange multipliers (alpha) for every observation vector.
	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.)
	NonBoundExamples	Gets the indices of the non-bounded examples (examples which have the corresponding Lagrange multipliers between 0 and C).
	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.)
	Shrinking	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.
	Strategy	Gets or sets the pair selection strategy to be used during optimization.
	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 1e-2.
	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. (Inherited from BaseSupportVectorClassificationTModel, TKernel, TInput.)
	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. (Overrides BaseSupportVectorClassificationTModel, TKernel, TInputInnerRun.)
	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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Reference

Accord.MachineLearning.VectorMachines.Learning Namespace

BaseSequentialMinimalOptimizationAddLanguageSpecificTextSet("LST96CD77E5_0?cs=&lt;|vb=(Of |cpp=&lt;|fs=&lt;'|nu=(");TModel, TKernel, TInputAddLanguageSpecificTextSet("LST96CD77E5_1?cs=&gt;|vb=)|cpp=&gt;|fs=&gt;|nu=)"); Class

Type Parameters

Reference

BaseSequentialMinimalOptimizationTModel, TKernel, TInput Class