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支持向量机分类 -凯发k8网页登录

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
classify observations using linear classification model
classify observations using error-correcting output codes (ecoc) classification model

函数

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
reduce size of machine learning model
templatesvmsupport vector machine template

修改模型

discard support vectors for linear support vector machine (svm) classifier
convert binary classification support vector machine (svm) model to incremental learner
resume training support vector machine (svm) classifier

解释模型

limelocal interpretable model-agnostic explanations (lime)
partialdependencecompute partial dependence
plotpartialdependencecreate partial dependence plot (pdp) and individual conditional expectation (ice) plots
shapleyshapley values

交叉验证

cross-validate machine learning model
classification edge for cross-validated classification model
classification loss for cross-validated classification model
classification margins for cross-validated classification model
classify observations in cross-validated classification model
cross-validate function for classification

测量性能

find classification error for support vector machine (svm) classifier
resubstitution classification loss
compare accuracies of two classification models using new data
find classification edge for support vector machine (svm) classifier
find classification margins for support vector machine (svm) classifier
resubstitution classification edge
resubstitution classification margin
compare accuracies of two classification models by repeated cross-validation
fit posterior probabilities
fit posterior probabilities for compact support vector machine (svm) classifier

为观测值分类

predictclassify observations using support vector machine (svm) classifier
classify training data using trained classifier

收集模型属性

gather properties of statistics and machine learning toolbox object from gpu
fitclinearfit binary linear classifier to high-dimensional data
predict labels for linear classification models
templatelinearlinear classification learner template
fit binary gaussian kernel classifier using random feature expansion
predict labels for gaussian kernel classification model
kernel model template
fitcecocfit multiclass models for support vector machines or other classifiers
predictclassify observations using multiclass error-correcting output codes (ecoc) model
error-correcting output codes learner template

类

classificationsvmsupport vector machine (svm) for one-class and binary classification
compactclassificationsvmcompact support vector machine (svm) for one-class and binary classification
cross-validated classification model
classificationlinearlinear model for binary classification of high-dimensional data
cross-validated linear model for binary classification of high-dimensional data
classificationkernelgaussian kernel classification model using random feature expansion
cross-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
cross-validated multiclass ecoc model for support vector machines (svms) and other classifiers
cross-validated linear error-correcting output codes model for multiclass classification of high-dimensional data
cross-validated kernel error-correcting output codes (ecoc) model for multiclass classification

主题


  • create and compare support vector machine (svm) classifiers, and export trained models to make predictions for new data.


  • 使用分离超平面和核变换通过 svm 执行二类分类。

  • predict class labels using classificationsvm predict block

    this example shows how to use the classificationsvm predict block for label prediction in simulink®.


  • this example shows how to use the classificationlinear predict block for label prediction in simulink®.


  • train an ecoc classification model, and then use the classificationecoc predict block for label prediction.

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