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LinearRegressionCoordinateDescent Class |
Coordinate descent algorithm for the L1 or L2-loss linear Support
Vector Regression (epsilon-SVR) learning problem in the dual form
(-s 12 and -s 13).
Inheritance HierarchySystemObject
Accord.MachineLearning.VectorMachines.LearningBaseSupportVectorRegressionSupportVectorMachine, Linear, Double
Accord.MachineLearning.VectorMachines.LearningBaseLinearRegressionCoordinateDescentSupportVectorMachine, Linear, Double
Accord.MachineLearning.VectorMachines.LearningLinearRegressionCoordinateDescent
Accord.MachineLearning.VectorMachines.LearningBaseSupportVectorRegressionSupportVectorMachine, Linear, Double
Accord.MachineLearning.VectorMachines.LearningBaseLinearRegressionCoordinateDescentSupportVectorMachine, Linear, Double
Accord.MachineLearning.VectorMachines.LearningLinearRegressionCoordinateDescent
Namespace: Accord.MachineLearning.VectorMachines.Learning
Assembly: Accord.MachineLearning (in Accord.MachineLearning.dll) Version: 3.8.0
Syntaxpublic class LinearRegressionCoordinateDescent : BaseLinearRegressionCoordinateDescent<SupportVectorMachine, Linear, double[]>
The LinearRegressionCoordinateDescent type exposes the following members.
Constructors| Name | Description | |
|---|---|---|
 | LinearRegressionCoordinateDescent |
Initializes a new instance of the LinearRegressionCoordinateDescent class.
|
| LinearRegressionCoordinateDescent(SupportVectorMachine, Double, Double) | Obsolete.
Obsolete.
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Properties| Name | Description | |
|---|---|---|
 | C |
Gets or sets the cost values associated with each input vector.
(Inherited from BaseSupportVectorRegressionTModel, 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 BaseSupportVectorRegressionTModel, TKernel, TInput.) |
| Epsilon |
Insensitivity zone ε. Increasing the value of ε can result in fewer
support vectors in the created model. Default value is 1e-3.
(Inherited from BaseSupportVectorRegressionTModel, TKernel, TInput.) |
| Inputs |
Gets or sets the input vectors for training.
(Inherited from BaseSupportVectorRegressionTModel, TKernel, TInput.) |
| IsLinear |
Gets whether the machine to be learned
has a Linear kernel.
(Inherited from BaseSupportVectorRegressionTModel, 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 BaseSupportVectorRegressionTModel, TKernel, TInput.) |
| Lagrange |
Gets the value for the Lagrange multipliers
(alpha) for every observation vector.
(Inherited from BaseLinearRegressionCoordinateDescentTModel, TKernel, TInput.) |
| Loss | (Inherited from BaseLinearRegressionCoordinateDescentTModel, TKernel, TInput.) |
| Model |
Gets the machine to be taught.
(Inherited from BaseSupportVectorRegressionTModel, TKernel, TInput.) |
| Outputs |
Gets or sets the output values for each calibration vector.
(Inherited from BaseSupportVectorRegressionTModel, TKernel, TInput.) |
| Token |
Gets or sets a cancellation token that can be used to
stop the learning algorithm while it is running.
(Inherited from BaseSupportVectorRegressionTModel, TKernel, TInput.) |
| Tolerance |
Convergence tolerance. Default value is 0.1.
(Inherited from BaseLinearRegressionCoordinateDescentTModel, 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 false.
(Inherited from BaseSupportVectorRegressionTModel, 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 BaseSupportVectorRegressionTModel, TKernel, TInput.) |
| Weights |
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.
(Inherited from BaseSupportVectorRegressionTModel, TKernel, TInput.) |
Methods| Name | Description | |
|---|---|---|
| ComputeError | Obsolete.
Obsolete.
(Inherited from BaseSupportVectorRegressionTModel, 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 BaseSupportVectorRegressionTModel, 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 BaseLinearRegressionCoordinateDescentTModel, TKernel, TInput.) |
| Learn |
Learns a model that can map the given inputs to the given outputs.
(Inherited from BaseSupportVectorRegressionTModel, TKernel, TInput.) |
| MemberwiseClone | Creates a shallow copy of the current Object. (Inherited from Object.) |
| Run | Obsolete.
Obsolete.
(Inherited from BaseSupportVectorRegressionTModel, 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.) |
RemarksThis class implements a SupportVectorMachine learning algorithm specifically crafted for linear machines only. It provides a L2-regularized, L1 or L2-loss coordinate descent learning algorithm for optimizing the dual form of learning. The code has been based on liblinear's method solve_l2r_l1l2_svc method, whose original description is provided below.
Liblinear's solver -s 12: L2R_L2LOSS_SVR_DUAL and -s 13: L2R_L1LOSS_SVR_DUAL. A coordinate descent algorithm for L1-loss and L2-loss linear epsilon-vector regression (epsilon-SVR).
min_\beta 0.5\beta^T (Q + diag(lambda)) \beta - p \sum_{i=1}^l|\beta_i| + \sum_{i=1}^l yi\beta_i, s.t. -upper_bound_i <= \beta_i <= upper_bound_i,
where Qij = yi yj xi^T xj and D is a diagonal matrix
In L1-SVM case:
upper_bound_i = C
lambda_i = 0In L2-SVM case:
upper_bound_i = INF lambda_i = 1/(2*C)
Given: x, y, p, C and eps as the stopping tolerance
See Algorithm 4 of Ho and Lin, 2012.
See Also