svm.xml

SVM的一个源程序

            <summary>
            Degree in kernel function (default 3).
            </summary>
        </member>
        <member name="P:SVM.Parameter.Gamma">
            <summary>
            Gamma in kernel function (default 1/k)
            </summary>
        </member>
        <member name="P:SVM.Parameter.Coefficient0">
            <summary>
            Zeroeth coefficient in kernel function (default 0)
            </summary>
        </member>
        <member name="P:SVM.Parameter.CacheSize">
            <summary>
            Cache memory size in MB (default 100)
            </summary>
        </member>
        <member name="P:SVM.Parameter.EPS">
            <summary>
            Tolerance of termination criterion (default 0.001)
            </summary>
        </member>
        <member name="P:SVM.Parameter.C">
            <summary>
            The parameter C of C-SVC, epsilon-SVR, and nu-SVR (default 1)
            </summary>
        </member>
        <member name="P:SVM.Parameter.WeightCount">
            <summary>
            Number of weights.
            </summary>
        </member>
        <member name="P:SVM.Parameter.WeightLabels">
            <summary>
            Array of indicies corresponding to the Weights array (for C-SVC)
            </summary>
        </member>
        <member name="P:SVM.Parameter.Weights">
            <summary>
            The parameter C of class i to weight*C in C-SVC (default 1)
            </summary>
        </member>
        <member name="P:SVM.Parameter.Nu">
            <summary>
            The parameter nu of nu-SVC, one-class SVM, and nu-SVR (default 0.5)
            </summary>
        </member>
        <member name="P:SVM.Parameter.P">
            <summary>
            The epsilon in loss function of epsilon-SVR (default 0.1)
            </summary>
        </member>
        <member name="P:SVM.Parameter.Shrinking">
            <summary>
            Whether to use the shrinking heuristics, (default True)
            </summary>
        </member>
        <member name="P:SVM.Parameter.Probability">
            <summary>
            Whether to train an SVC or SVR model for probability estimates, (default False)
            </summary>
        </member>
        <member name="T:SVM.Node">
            <summary>
            Encapsulates a node in a Problem vector, with an index and a value (for more efficient representation
            of sparse data.
            </summary>
        </member>
        <member name="M:SVM.Node.#ctor">
            <summary>
            Default Constructor.
            </summary>
        </member>
        <member name="M:SVM.Node.#ctor(System.Int32,System.Double)">
            <summary>
            Constructor.
            </summary>
            <param name="index">The index of the value.</param>
            <param name="value">The value to store.</param>
        </member>
        <member name="M:SVM.Node.ToString">
            <summary>
            String representation of this Node as {index}:{value}.
            </summary>
            <returns>{index}:{value}</returns>
        </member>
        <member name="P:SVM.Node.Index">
            <summary>
            Index of this Node.
            </summary>
        </member>
        <member name="P:SVM.Node.Value">
            <summary>
            Value at Index.
            </summary>
        </member>
        <member name="T:SVM.Training">
            <summary>
            Class containing the routines to train SVM models.
            </summary>
        </member>
        <member name="M:SVM.Training.Train(System.String[])">
            <summary>
            Legacy.  Allows use as if this was svm_train.  See libsvm documentation for details on which arguments to pass.
            </summary>
            <param name="args"></param>
        </member>
        <member name="M:SVM.Training.PerformCrossValidation(SVM.Problem,SVM.Parameter,System.Int32)">
            <summary>
            Performs cross validation.
            </summary>
            <param name="problem">The training data</param>
            <param name="parameters">The parameters to test</param>
            <param name="nrfold">The number of cross validations to use</param>
            <returns>The cross validation score</returns>
        </member>
        <member name="M:SVM.Training.Train(SVM.Problem,SVM.Parameter)">
            <summary>
            Trains a model using the provided training data and parameters.
            </summary>
            <param name="problem">The training data</param>
            <param name="parameters">The parameters to use</param>
            <returns>A trained SVM Model</returns>
        </member>
        <member name="T:SVM.Model">
            <summary>
            Encapsulates an SVM Model.
            </summary>
        </member>
        <member name="M:SVM.Model.Read(System.String)">
            <summary>
            Reads a Model from the provided file.
            </summary>
            <param name="filename">The name of the file containing the Model</param>
            <returns>the Model</returns>
        </member>
        <member name="M:SVM.Model.Read(System.IO.Stream)">
            <summary>
            Reads a Model from the provided stream.
            </summary>
            <param name="stream">The stream from which to read the Model.</param>
            <returns>the Model</returns>
        </member>
        <member name="M:SVM.Model.Write(System.String,SVM.Model)">
            <summary>
            Writes a model to the provided filename.  This will overwrite any previous data in the file.
            </summary>
            <param name="filename">The desired file</param>
            <param name="model">The Model to write</param>
        </member>
        <member name="M:SVM.Model.Write(System.IO.Stream,SVM.Model)">
            <summary>
            Writes a model to the provided stream.
            </summary>
            <param name="stream">The output stream</param>
            <param name="model">The model to write</param>
        </member>
        <member name="P:SVM.Model.Parameter">
            <summary>
            Parameter object.
            </summary>
        </member>
        <member name="P:SVM.Model.NumberOfClasses">
            <summary>
            Number of classes in the model.
            </summary>
        </member>
        <member name="P:SVM.Model.SupportVectorCount">
            <summary>
            Total number of support vectors.
            </summary>
        </member>
        <member name="P:SVM.Model.SupportVectors">
            <summary>
            The support vectors.
            </summary>
        </member>
        <member name="P:SVM.Model.SupportVectorCoefficients">
            <summary>
            The coefficients for the support vectors.
            </summary>
        </member>
        <member name="P:SVM.Model.Rho">
            <summary>
            Rho values.
            </summary>
        </member>
        <member name="P:SVM.Model.PairwiseProbabilityA">
            <summary>
            First pairwise probability.
            </summary>
        </member>
        <member name="P:SVM.Model.PairwiseProbabilityB">
            <summary>
            Second pairwise probability.
            </summary>
        </member>
        <member name="P:SVM.Model.ClassLabels">
            <summary>
            Class labels.
            </summary>
        </member>
        <member name="P:SVM.Model.NumberOfSVPerClass">
            <summary>
            Number of support vectors per class.
            </summary>
        </member>
        <member name="T:SVM.Scaling">
            <summary>
            Deals with the scaling of Problems so they have uniform ranges across all dimensions in order to
            result in better SVM performance.
            </summary>
        </member>
        <member name="F:SVM.Scaling.DEFAULT_LOWER_BOUND">
            <summary>
            Default lower bound for scaling (-1).
            </summary>
        </member>
        <member name="F:SVM.Scaling.DEFAULT_UPPER_BOUND">
            <summary>
            Default upper bound for scaling (1).
            </summary>
        </member>
        <member name="M:SVM.Scaling.DetermineRange(SVM.Problem)">
            <summary>
            Determines the Range transform for the provided problem.  Uses the default lower and upper bounds.
            </summary>
            <param name="prob">The Problem to analyze</param>
            <returns>The Range transform for the problem</returns>
        </member>
        <member name="M:SVM.Scaling.DetermineRangeTransform(SVM.Problem,System.Double,System.Double)">
            <summary>
            Determines the Range transform for the provided problem.
            </summary>
            <param name="prob">The Problem to analyze</param>
            <param name="lowerBound">The lower bound for scaling</param>
            <param name="upperBound">The upper bound for scaling</param>