|
LeastSquaresLearningTKernel, TInput Class |
Least Squares SVM (LS-SVM) learning algorithm.
Inheritance HierarchySystemObject
Accord.MachineLearningBinaryLearningBaseSupportVectorMachineTKernel, TInput, TInput
Accord.MachineLearning.VectorMachines.LearningBaseSupportVectorClassificationSupportVectorMachineTKernel, TInput, TKernel, TInput
Accord.MachineLearning.VectorMachines.LearningLeastSquaresLearningBaseSupportVectorMachineTKernel, TInput, TKernel, TInput
Accord.MachineLearning.VectorMachines.LearningLeastSquaresLearningTKernel, TInput
Accord.MachineLearningBinaryLearningBaseSupportVectorMachineTKernel, TInput, TInput
Accord.MachineLearning.VectorMachines.LearningBaseSupportVectorClassificationSupportVectorMachineTKernel, TInput, TKernel, TInput
Accord.MachineLearning.VectorMachines.LearningLeastSquaresLearningBaseSupportVectorMachineTKernel, TInput, TKernel, TInput
Accord.MachineLearning.VectorMachines.LearningLeastSquaresLearningTKernel, TInput
Namespace: Accord.MachineLearning.VectorMachines.Learning
Assembly: Accord.MachineLearning (in Accord.MachineLearning.dll) Version: 3.8.0
Syntaxpublic class LeastSquaresLearning<TKernel, TInput> : LeastSquaresLearningBase<SupportVectorMachine<TKernel, TInput>, TKernel, TInput> where TKernel : Object, IKernel<TInput> where TInput : ICloneable
Type Parameters
- TKernel
- TInput
The LeastSquaresLearningTKernel, TInput type exposes the following members.
Constructors| Name | Description | |
|---|---|---|
 | LeastSquaresLearningTKernel, TInput | Initializes a new instance of the LeastSquaresLearningTKernel, TInput class |
Properties| Name | Description | |
|---|---|---|
 | 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
stored the kernel matrix. Default is the
same number of input vectors.
(Inherited from LeastSquaresLearningBaseTModel, 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.) |
| 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 1e-6.
(Inherited from LeastSquaresLearningBaseTModel, 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.) |
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 main body of the learning algorithm.
(Inherited from LeastSquaresLearningBaseTModel, 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.) |
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.) |
RemarksSee Also