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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Zelinka, J., Romportl, J., Müller, L. (2010). A Priori and A Posteriori Machine Learning and Nonlinear Artificial Neural Networks. In: Sojka, P., Horák, A., Kopeček, I., Pala, K. (eds) Text, Speech and Dialogue. TSD 2010. Lecture Notes in Computer Science(), vol 6231. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-15760-8_60
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DOI: https://doi.org/10.1007/978-3-642-15760-8_60
Publisher Name: Springer, Berlin, Heidelberg
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