spider20060724

所属分类:matlab编程
开发工具:matlab
文件大小:4863KB
下载次数:167
上传日期:2013-11-02 21:13:57
上 传 者Compile_Z
说明:  机器学习和模式识别工具包spider。内容很丰富。包含svm 决策树(C45,J48)、svm、knn、adaboost、bagging、hmm(隐马尔科夫模型)、随机树(random forest)等
(Machine learning and pattern recognition toolkit spider. Very rich in contents. Tree contains svm (C45, J48), svm, knn, adaboost, bagging, hmm (hidden Markov model), random trees (random forest), etc.)

文件列表:
spider_jul2406\spider\basic\@algorithm\algorithm.m (2941, 2004-03-31)
spider_jul2406\spider\basic\@algorithm\am_i_data.m (154, 2003-05-12)
spider_jul2406\spider\basic\@algorithm\display.m (1434, 2003-05-12)
spider_jul2406\spider\basic\@algorithm\display_simple.m (1526, 2003-05-12)
spider_jul2406\spider\basic\@algorithm\extract.m (2111, 2004-03-11)
spider_jul2406\spider\basic\@algorithm\gen.m (254, 2003-05-12)
spider_jul2406\spider\basic\@algorithm\get_name.m (155, 2004-03-26)
spider_jul2406\spider\basic\@algorithm\isdeferred.m (56, 2004-03-23)
spider_jul2406\spider\basic\@algorithm\subsasgn.m (3354, 2004-11-18)
spider_jul2406\spider\basic\@algorithm\subsref.m (2943, 2004-08-23)
spider_jul2406\spider\basic\@algorithm\test.m (2211, 2004-12-02)
spider_jul2406\spider\basic\@algorithm\train.m (3690, 2004-11-18)
spider_jul2406\spider\basic\@algorithm\traintest.m (3929, 2004-03-26)
spider_jul2406\spider\basic\@algorithm\traintests.m (437, 2003-05-12)
spider_jul2406\spider\basic\@chain\chain.m (1112, 2006-07-24)
spider_jul2406\spider\basic\@chain\display.m (1461, 2003-05-12)
spider_jul2406\spider\basic\@chain\subsasgn.m (3417, 2004-08-23)
spider_jul2406\spider\basic\@chain\subsref.m (2998, 2004-08-23)
spider_jul2406\spider\basic\@chain\testing.m (105, 2003-05-12)
spider_jul2406\spider\basic\@chain\training.m (249, 2003-05-12)
spider_jul2406\spider\basic\@cv\cv.m (2334, 2006-07-24)
spider_jul2406\spider\basic\@cv\display.m (1829, 2003-05-12)
spider_jul2406\spider\basic\@cv\get_name.m (372, 2003-05-12)
spider_jul2406\spider\basic\@cv\subsasgn.m (3418, 2004-08-23)
spider_jul2406\spider\basic\@cv\subsref.m (3007, 2004-08-23)
spider_jul2406\spider\basic\@cv\testing.m (640, 2003-05-12)
spider_jul2406\spider\basic\@cv\training.m (4370, 2006-02-08)
spider_jul2406\spider\basic\@data\concatenate.m (720, 2003-05-12)
spider_jul2406\spider\basic\@data\data.asv (2514, 2006-02-18)
spider_jul2406\spider\basic\@data\data.m (2569, 2006-07-24)
spider_jul2406\spider\basic\@data\display.m (706, 2005-05-18)
spider_jul2406\spider\basic\@data\get.m (684, 2006-05-31)
spider_jul2406\spider\basic\@data\get_dim.m (653, 2006-07-19)
spider_jul2406\spider\basic\@data\get_index.m (71, 2003-05-12)
spider_jul2406\spider\basic\@data\get_name.m (213, 2004-03-26)
spider_jul2406\spider\basic\@data\get_x.m (431, 2003-05-12)
spider_jul2406\spider\basic\@data\get_xy.m (420, 2003-05-12)
spider_jul2406\spider\basic\@data\get_y.m (388, 2003-05-12)
spider_jul2406\spider\basic\@data\isdeferred.m (35, 2004-11-08)
spider_jul2406\spider\basic\@data\partition.m (2294, 2003-08-12)
... ...

This is a template object, copying this directory and editing it should make it easy to create new objects. It implements a simple SVM for pattern recognition. The files are the following: template.m % constructor -- needs to include all hyperparameter and model % parameter declarations training.m % these two files are the core of any object -- they train testing.m % the algorithm given data and test the learnt model get_name.m % used for printing the name, you only need to edit this file % if you want to output special hyperparameters of your object % when the name is printed out subsref.m % used for getting parameters, you do not need to edit this file subsasgn.m% used for setting parameters, you do not need to edit this file PROGRAMMING GUIDELINES: 1) If you use a kernel, try to call train and test members of kernel, see e.g the svm object. This will help to make complex kernel objects, e.g feed_fwd networks of kernels. 2) always use get, get_x, set_x of data objects, and try not to create new data objects, otherwise you may lose e.g indexing information if you do not copy it -- see e.g svm object.

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