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comparison doc/v2_planning/existing_python_ml_libraries.txt @ 1309:e5b7a7913329

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fix rst error.
author Frederic Bastien <nouiz@nouiz.org>
date Tue, 05 Oct 2010 12:26:02 -0400
parents 53937045f6c7
children f5e9c00a67d7
comparison
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1308:d5e536338b69 1309:e5b7a7913329
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