LinearSvmTrainer 类

定义

public sealed class LinearSvmTrainer : Microsoft.ML.Trainers.OnlineLinearTrainer<Microsoft.ML.Data.BinaryPredictionTransformer<Microsoft.ML.Trainers.LinearBinaryModelParameters>,Microsoft.ML.Trainers.LinearBinaryModelParameters>

type LinearSvmTrainer = class
    inherit OnlineLinearTrainer<BinaryPredictionTransformer<LinearBinaryModelParameters>, LinearBinaryModelParameters>

Public NotInheritable Class LinearSvmTrainer
Inherits OnlineLinearTrainer(Of BinaryPredictionTransformer(Of LinearBinaryModelParameters), LinearBinaryModelParameters)

继承

Object

TrainerEstimatorBase<TTransformer,TModel>

OnlineLinearTrainer<BinaryPredictionTransformer<LinearBinaryModelParameters>,LinearBinaryModelParameters>

LinearSvmTrainer

注解

To create this trainer, use LinearSvm or LinearSvm(Options).

Input and Output Columns

The input label column data must be Boolean. The input features column data must be a known-sized vector of Single. This trainer outputs the following columns:

Trainer Characteristics

Training Algorithm Details

Linear SVM implements an algorithm that finds a hyperplane in the feature space for binary classification, by solving an SVM problem. For instance, with feature values $f_0, f_1,..., f_{D-1}$, the prediction is given by determining what side of the hyperplane the point falls into. That is the same as the sign of the feautures' weighted sum, i.e. $\sum_{i = 0}^{D-1} \left(w_i * f_i \right) + b$, where $w_0, w_1,..., w_{D-1}$ are the weights computed by the algorithm, and $b$ is the bias computed by the algorithm.

Linear SVM implements the PEGASOS method, which alternates between stochastic gradient descent steps and projection steps, introduced in this paper by Shalev-Shwartz, Singer and Srebro.

Check the See Also section for links to usage examples.

字段

属性

方法

扩展方法

适用于

另请参阅

• LinearSvm(BinaryClassificationCatalog+BinaryClassificationTrainers, LinearSvmTrainer+Options)
• LinearSvm(BinaryClassificationCatalog+BinaryClassificationTrainers, String, String, String, Int32)