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[分享] 支持向量机 Support Vector Machine 程序网址

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楼主
发表于 2016-8-30 21:29:24 | 只看该作者 回帖奖励 |倒序浏览 |阅读模式
支持向量机 Support Vector Machine 程序网址  
(1)Least Squares Support Vector Machines (LS-SVM)
http://www.esat.kuleuven.be/sista/lssvmlab/
            
Support Vector Machines is a powerful methodology for solving problems in nonlinear classification, function estimation and density estimation which has also led to many other recent developments in kernel based methods in general.
           
Latest version:
LS-SVMlab v1.8 (August 16, 2011)

Book reference:

J.A.K. Suykens, T. Van Gestel, J. De Brabanter, B. De Moor, J. Vandewalle, Least Squares Support Vector Machines, World Scientific, Singapore, 2002 (ISBN 981-238-151-1)
      
(2)LIBSVM -- A Library for Support Vector Machines
Chih-Chung Chang and Chih-Jen Lin
http://www.csie.ntu.edu.tw/~cjlin/libsvm/

              
LIBSVM is an integrated software for support vector classification, (C-SVC, nu-SVC), regression (epsilon-SVR, nu-SVR) and distribution estimation (one-class SVM). It supports multi-class classification.
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沙发
 楼主| 发表于 2016-8-30 21:34:47 | 只看该作者

极限学习机 Extreme Learning Machines (ELM) 程序网址

极限学习机 Extreme Learning Machines (ELM) 程序网址http://www.ntu.edu.sg/home/egbhuang/
Extreme Learning Machines (ELM): Filling the Gap between Frank Rosenblatt's Dream and John von Neumann's Puzzle
- Network architectures: a homogenous hierarchical learning machine for partially or fully connected multi layers / single layer of (artifical or biological) networks with almost any type of practical (artifical) hidden nodes (or bilogical neurons).
- Learning theories: Learning can be made without iteratively tuning (articial) hidden nodes (or biological neurons).
- Learning algorithms: General, unifying and universal (optimization based) learning frameworks for compression, feature learning, clustering, regression and classification. Basic steps:
1) Learning are made layer wise (in white box)
2) Randomly generate (any nonliear piecewise) hidden neurons or inheritate hidden neuorns from ancestors
3) Learn the output weights in each hidden layer (with application based optimization constraints)

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板凳
发表于 2016-10-18 17:53:18 | 只看该作者
谢谢,好东西!!!
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5#
 楼主| 发表于 7 天前 | 只看该作者
danfengxiaoz 发表于 2016-10-18 17:53
谢谢,好东西!!!


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