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支持向量机分类

用于二类分类或多类分类的支持向量机

为了提高在中低维数据集上的准确度并增加核函数选择，可以使用 Classification Learner App 训练二类 SVM 模型，或包含 SVM 二类学习器的多类纠错输出编码 (ECOC) 模型。为了获得更大的灵活性，可以在命令行界面中使用 fitcsvm 训练二类 SVM 模型，或者使用 fitcecoc 训练由二类 SVM 学习器组成的多类 ECOC 模型。

为了减少在高维数据集上的计算时间，可以使用 fitclinear 高效地训练二类线性分类模型（例如线性 SVM 模型），或者使用 fitcecoc 训练由 SVM 模型组成的多类 ECOC 模型。

对于大数据的非线性分类，可以使用 fitckernel 训练二类高斯核分类模型。

App

Classification Learner使用有监督的机器学习训练模型以对数据进行分类

函数

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fitcsvm训练用于一类和二类分类的支持向量机 (SVM) 分类器
fitSVMPosteriorFit posterior probabilities
predictClassify observations using support vector machine (SVM) classifier
templateSVMSupport vector machine template
fitclinearFit linear classification model to high-dimensional data
predictPredict labels for linear classification models
templateLinearLinear classification learner template
fitckernelFit Gaussian kernel classification model using random feature expansion
predictPredict labels for Gaussian kernel classification model
templateKernelKernel model template
fitcecocFit multiclass models for support vector machines or other classifiers
predictClassify observations using multiclass error-correcting output codes (ECOC) model
templateECOCError-correcting output codes learner template

类

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ClassificationSVMSupport vector machine (SVM) for one-class and binary classification
CompactClassificationSVMCompact support vector machine (SVM) for one-class and binary classification
ClassificationPartitionedModelCross-validated classification model
ClassificationLinearLinear model for binary classification of high-dimensional data
ClassificationPartitionedLinearCross-validated linear model for binary classification of high-dimensional data
ClassificationKernelGaussian kernel classification model using random feature expansion
ClassificationPartitionedKernelCross-validated, binary kernel classification model
ClassificationECOCMulticlass model for support vector machines (SVMs) and other classifiers
CompactClassificationECOCCompact multiclass model for support vector machines (SVMs) and other classifiers
ClassificationPartitionedECOCCross-validated multiclass ECOC model for support vector machines (SVMs) and other classifiers
ClassificationPartitionedLinearECOCCross-validated linear error-correcting output codes model for multiclass classification of high-dimensional data
ClassificationPartitionedKernelECOCCross-validated kernel error-correcting output codes (ECOC) model for multiclass classification

主题

Train Support Vector Machines Using Classification Learner App

Create and compare support vector machine (SVM) classifiers, and export trained models to make predictions for new data.

用于二类分类的支持向量机

使用分离超平面和核变换通过 SVM 执行二类分类。

相关信息

Statistics and Machine Learning Toolbox 文档

支持

Mastering Machine Learning: A Step-by-Step Guide with MATLAB

Mastering Machine Learning: A Step-by-Step Guide with MATLAB

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