machine learning - algorithm for building multi-class (versus binary) classifiers -


i student , doing research text classification. have read several paper that. found many people using naive bayesian classifier.

i have 4 class classify. , read svm can classify thing 2 class,..yes/no 1/0

is there algorithm besides nbc algorithm building classifiers separate data more than 2 classes?

most ml techniques capable of building multi-class classifiers, instance:

  • decision tree (eg c4.5)

  • neural network via softmax (aka multi-layer perceptron, or mlp)

  • lda (linear discriminant analysis)

  • naive bayes

support vector machines routinely used multi-class classification (see example excellent scikit-learn library), using instance, "one-against-many" inductive approach. in other words, data trained on first svm separate data class versus else. "everything else" data passed second svm separates data class ii versus else, , on.


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