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- Feature_learning abstract "Feature learning or representation learning is a set of techniques in machine learning that learn a transformation of "raw" inputs to a representation that can be effectively exploited in a supervised learning task such as classification. Feature learning algorithms themselves may be either unsupervised or supervised, and include autoencoders,dictionary learning, matrix factorization,restricted Boltzmann machines and various form of clustering.Multilayer neural networks can also be considered to perform feature learning, since they learn a representation of their input at the hidden layer(s) which is subsequently used for classification or regression at the output layer, and feature learning is an integral part of deep learning, to the point that the two are sometimes considered synonyms. (By contrast, kernel methods such as the support vector machine compute a fixed transformation of their inputs by means of a kernel function, and do not perform feature learning.)When the feature learning can be performed in an unsupervised way, it enables a form of semisupervised learning where first, features are learned from an unlabeled dataset, which are then employed to improve performance in a supervised setting with labeled data.".
- Feature_learning wikiPageID "38870173".
- Feature_learning wikiPageRevisionID "603576854".
- Feature_learning hasPhotoCollection Feature_learning.
- Feature_learning subject Category:Machine_learning.
- Feature_learning comment "Feature learning or representation learning is a set of techniques in machine learning that learn a transformation of "raw" inputs to a representation that can be effectively exploited in a supervised learning task such as classification.".
- Feature_learning label "Feature learning".
- Feature_learning sameAs m.0ryvx64.
- Feature_learning sameAs Q17013334.
- Feature_learning sameAs Q17013334.
- Feature_learning wasDerivedFrom Feature_learning?oldid=603576854.
- Feature_learning isPrimaryTopicOf Feature_learning.