COPYRIGHT YEAR

2010

AUTHORS

Jan Romportl, Jan Zelinka, Luděk Müller

TITLE

A Priori and A Posteriori Machine Learning and Nonlinear Artificial Neural Networks

ABSTRACT

The main idea of a priori machine learning is to apply a machine learning method on a machine learning problem itself. We call it “a priori” because the processed data set does not originate from any measurement or other observation. Machine learning which deals with any observation is called “posterior”. The paper describes how posterior machine learning can be modified by a priori machine learning. A priori and posterior machine learning algorithms are proposed for artificial neural network training and are tested in the task of audio-visual phoneme classification.

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27 TRIPLES      25 PREDICATES      24 URIs      13 LITERALS

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19 sg:metadataRights OpenAccess
20 sg:pageFirst 472
21 sg:pageLast 479
22 sg:scigraphId d96f89d7b4023c73e0768caea15fa7a8
23 sg:title A Priori and A Posteriori Machine Learning and Nonlinear Artificial Neural Networks
24 sg:webpage https://link.springer.com/10.1007/978-3-642-15760-8_60
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26 rdfs:label BookChapter: A Priori and A Posteriori Machine Learning and Nonlinear Artificial Neural Networks
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