Wednesday, August 10, 2016

Multi-class and Multi-label classification

In machine learning, multiclass or multinomial classification is the problem of classifying instances into one of the more than two classes (classifying instances into one of the two classes is called binary classification).
While some classification algorithms naturally permit the use of more than two classes, others are by nature binary algorithms; these can, however, be turned into multinomial classifiers by a variety of strategies.
Multiclass classification should not be confused with multi-label classification, where multiple labels are to be predicted for each instance (for example: each image).


http://scikit-learn.org/stable/modules/multiclass.html


Implementation in Caffe

https://github.com/BVLC/caffe/pull/3471

http://nbviewer.jupyter.org/github/BVLC/caffe/blob/master/examples/pascal-multilabel-with-datalayer.ipynb

http://stackoverflow.com/questions/32680860/caffe-with-multi-label-images


mxnet

https://github.com/dmlc/mxnet/issues/1404
https://github.com/dmlc/mxnet/blob/a6b4baf9824bb3f0bfb8ec804d333913b3bbc0c8/doc/python/io.md
https://github.com/dmlc/mxnet/issues/1758
https://github.com/jimxinbo/multilabel-layer-mxnet
https://github.com/dmlc/mxnet/issues/945






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