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by Dn_Ab
5637 days ago
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There are lots of techniques. Really simple and effective methods are naive bayes and kNN. Other methods include decision trees and logistic regression which seems to be popular at Google from what I can indirectly infer from their papers. More complex methods include bayesian and non bayesian graphical models, kernel methods (SVM, RVM) and not too hard but powerful methods are boosted trees, random forests and ensemble methods in general. Layers of unsupervised learners (clustering) feeding into a supervised learner form a very powerful technique known as deep learning. This technique hasn't found a niche though and can be outperformed by shallower methods for much of where they are used [1]. And due to all this big data mumbo jumbo on-line learning methods are getting to be more important. [1] http://ai.stanford.edu/~ang/papers/nipsdlufl10-AnalysisSingl... |
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