Search - L2 SVM

Search - L2 SVM - List

[AI-NN-PR] libsvm-2.89 DL : 0: 是一種線性方成的分類器。SVM透過統計的方式將雜亂的資料以NN的方式分成兩類，以便處理。LIBLINEAR is a linear classifier for data with millions of instances and features. It supports L2-regularized logistic regression (LR), L2-loss linear SVM, and L1-loss linear SVM. -Main features of LIBLINEAR include Same data format as LIBSVM, our general-purpose SVM solver, and also similar usage Multi-class classification: 1) one-vs-the rest, 2) Crammer & Singer Cross validation for model selection Probability estimates (logistic regression only) Weights for unbalanced data MATLAB/Octave, Java interfaces
Update : 2025-04-26 Size : 509kb Publisher : 陳彥霖
[AI-NN-PR] bsvm-2.08 DL : 0: BSVM解决了支持向量机（SVM），用于解决大型分类和回归问题。它包括以下方法一个对一个使用约束约束公式的多类分类通过解决单一优化问题（再次，有界公式）进行多类分类。参见我们比较文件的第3节。使用Crammer和Singer的配方进行多级分类。参见我们的比较文章第4节。使用约束约束公式的回归-BSVM solves support vector machines (SVM) for the solution of large classification and regression problems. It includes the following methods One vs. One multi-class classification using a bound-constrained formulation Multi-class classification by solving a single optimization problem (again, a bounded formulation). See Section 3 of our comparison paper. Multi-class classification using Crammer and Singer s formulation. See Section 4 of our comparison paper. Regression using a bound-constrained formulation Multi-class classification using Crammer and Singer s formulation with squared hinge (L2) loss
Update : 2025-04-26 Size : 364kb Publisher : 程