A perspective view and survey of meta-learning
Artificial Intelligence Review
Empirical Bayes for Learning to Learn
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
Metalearning: Applications to Data Mining
Metalearning: Applications to Data Mining
Gaussian sum approach with optimal experiment design for neural network
SIP '07 Proceedings of the Ninth IASTED International Conference on Signal and Image Processing
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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.