ProbabilisticCoordinateDescentTKernel, TInput Class 
Namespace: Accord.MachineLearning.VectorMachines.Learning
public class ProbabilisticCoordinateDescent<TKernel, TInput> : BaseProbabilisticCoordinateDescent<SupportVectorMachine<TKernel, TInput>, TKernel, TInput> where TKernel : struct, new(), ILinear<TInput>
The ProbabilisticCoordinateDescentTKernel, TInput type exposes the following members.
Name  Description  

ProbabilisticCoordinateDescentTKernel, TInput 
Constructs a new Newton method algorithm for L1regularized
logistic regression (probabilistic linear vector machine).

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 BaseProbabilisticCoordinateDescentTModel, TKernel, TInput.)  
MaximumLineSearches 
Gets or sets the maximum number of line searches
that can be performed per iteration. Default is 20.
(Inherited from BaseProbabilisticCoordinateDescentTModel, TKernel, TInput.)  
MaximumNewtonIterations 
Gets or sets the maximum number of inner iterations that can
be performed by the inner solver algorithm. Default is 100.
(Inherited from BaseProbabilisticCoordinateDescentTModel, 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 0.01.
(Inherited from BaseProbabilisticCoordinateDescentTModel, 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.) 
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 BaseProbabilisticCoordinateDescentTModel, 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.) 
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.) 
This class implements a SupportVectorMachine learning algorithm specifically crafted for probabilistic linear machines only. It provides a L1 regularized coordinate descent learning algorithm for optimizing the learning problem. The code has been based on liblinear's method solve_l1r_lr method, whose original description is provided below.
Liblinear's solver s 6: L1R_LR. A coordinate descent algorithm for L1regularized logistic regression (probabilistic svm) problems.
min_w \sum wj + C \sum log(1+exp(yi w^T xi)),
Given: x, y, Cp, Cn, and eps as the stopping tolerance
See Yuan et al. (2011) and appendix of LIBLINEAR paper, Fan et al. (2008)