【Stanford-ML-Discussion】二值分类器的多值分类问题理解

最新推荐文章于 2025-01-22 08:30:00 发布

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最新推荐文章于 2025-01-22 08:30:00 发布 · 2.2k 阅读

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SVM是一种基于统计学习理论的分类系统，通过最大化类别间边界来划分样本。它可以采用非线性核函数转化为非线性分类器。在多类分类中，SVM可通过one-vs-one或one-vs-all策略，结合多个二分类器实现。ENVI Classic的SVM实现采用pairwise策略，并提供概率估计。分类时选择概率最高的类别。此外，还提及了Logistic Regression和Neural Network的多分类方法。

引用自ENVI帮助文档

SVM is a classification system derived from statistical learning theory. It separates the classes with a decision surface that maximizes the margin between the classes. The surface is often called the optimal hyperplane, and the data points closest to the hyperplane are called support vectors. The support vectors are the critical elements of the training set.
You can adapt SVM to become a nonlinear classifier through the use of nonlinear kernels. While SVM is a binary classifier in its simplest form, it can function as a multiclass classifier by combining several binary SVM classifiers (creating a binary classifier for each possible pair of classes). ENVI Classic’s implementation of SVM uses the pairwise classification strategy for multiclass classification. SVM classification output is the decision values of each pixel for each class, which are used for probability estimates. The probability values, stored in ENVI Classic as rule images, repr