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Description: 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
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Author: 陳彥霖 |
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Description: 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
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Size: 372736 |
Author: 程 |
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