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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，可以使用分类学习器训练二类 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

分类学习器使用有监督的机器学习训练模型以对数据进行分类

模块

ClassificationSVM PredictClassify observations using support vector machine (SVM) classifier for one-class and binary classification (自 R2020b 起)
ClassificationECOC PredictClassify observations using error-correcting output codes (ECOC) classification model (自 R2023a 起)
ClassificationLinear PredictClassify observations using linear classification model (自 R2023a 起)
IncrementalClassificationLinear PredictClassify observations using incremental linear classification model (自 R2023b 起)
IncrementalClassificationLinear FitFit incremental linear binary classification model (自 R2023b 起)
IncrementalClassificationECOC PredictClassify observations using incremental ECOC classification model (自 R2024a 起)
IncrementalClassificationECOC FitFit incremental ECOC classification model (自 R2024a 起)
Update MetricsUpdate performance metrics in incremental learning model given new data (自 R2023b 起)

函数

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创建模型或模板

fitcsvm训练用于一类和二类分类的支持向量机 (SVM) 分类器
compactReduce size of machine learning model
templateSVMSupport vector machine template

修改模型

discardSupportVectorsDiscard support vectors for linear support vector machine (SVM) classifier
incrementalLearnerConvert binary classification support vector machine (SVM) model to incremental learner (自 R2020b 起)
resumeResume training support vector machine (SVM) classifier

解释模型

limeLocal interpretable model-agnostic explanations (LIME) (自 R2020b 起)
partialDependenceCompute partial dependence (自 R2020b 起)
permutationImportancePredictor importance by permutation (自 R2024a 起)
plotPartialDependenceCreate partial dependence plot (PDP) and individual conditional expectation (ICE) plots
shapleyShapley values (自 R2021a 起)

交叉验证

crossvalCross-validate machine learning model
kfoldEdgeClassification edge for cross-validated classification model
kfoldLossClassification loss for cross-validated classification model
kfoldMarginClassification margins for cross-validated classification model
kfoldPredictClassify observations in cross-validated classification model
kfoldfunCross-validate function for classification

测量性能

lossFind classification error for support vector machine (SVM) classifier
resubLossResubstitution classification loss
compareHoldoutCompare accuracies of two classification models using new data
edgeFind classification edge for support vector machine (SVM) classifier
marginFind classification margins for support vector machine (SVM) classifier
resubEdgeResubstitution classification edge
resubMarginResubstitution classification margin
testckfoldCompare accuracies of two classification models by repeated cross-validation
fitSVMPosteriorFit posterior probabilities
fitPosteriorFit posterior probabilities for compact support vector machine (SVM) classifier

为观测值分类

predictClassify observations using support vector machine (SVM) classifier
resubPredictClassify training data using trained classifier

收集模型属性

gatherGather properties of Statistics and Machine Learning Toolbox object from GPU (自 R2020b 起)
fitclinearFit binary linear classifier to high-dimensional data
predictPredict labels for linear classification models
templateLinearLinear learner template
fitckernelFit binary Gaussian kernel classifier using random feature expansion
predictPredict labels for Gaussian kernel classification model
templateKernelKernel learner 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

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