Mercurial > pylearn
comparison doc/v2_planning/existing_python_ml_libraries.txt @ 1309:e5b7a7913329
fix rst error.
author | Frederic Bastien <nouiz@nouiz.org> |
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date | Tue, 05 Oct 2010 12:26:02 -0400 |
parents | 53937045f6c7 |
children | f5e9c00a67d7 |
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1308:d5e536338b69 | 1309:e5b7a7913329 |
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99 shogun: | 99 shogun: |
100 large scale kernel learning (mostly svms). this wraps other | 100 large scale kernel learning (mostly svms). this wraps other |
101 libraries we should definitely be interested in, such as libsvm | 101 libraries we should definitely be interested in, such as libsvm |
102 (because it is well-established) and others that get state of the art | 102 (because it is well-established) and others that get state of the art |
103 performance or are good for extremely large datasets, etc. | 103 performance or are good for extremely large datasets, etc. |
104 milk: | |
105 k-means | |
106 svm's with arbitrary python types for kernel arguments | |
107 pybrain: | |
108 lstm | |
109 mlpy: | |
110 feature selection | |
111 mdp: | |
112 ica | |
113 LLE | |
114 scikit.learn: | |
115 lasso | |
116 nearest neighbor | |
117 isomap | |
118 various metrics | |
119 mean shift | |
120 cross validation | |
121 LDA | |
122 HMMs | |
123 Yet Another Python Graph Library: | |
124 graph similarity functions that could be useful if we want to | |
125 learn with graphs as data | |
126 | 104 |
105 * milk: | |
106 * k-means | |
107 * svm's with arbitrary python types for kernel arguments | |
108 * pybrain: | |
109 * lstm | |
110 * mlpy: | |
111 * feature selection | |
112 * mdp: | |
113 * ica | |
114 * LLE | |
115 * scikit.learn: | |
116 * lasso | |
117 * nearest neighbor | |
118 * isomap | |
119 * various metrics | |
120 * mean shift | |
121 * cross validation | |
122 * LDA | |
123 * HMMs | |
124 * Yet Another Python Graph Library: | |
125 * graph similarity functions that could be useful if we want to | |
126 learn with graphs as data | |
127 |