Abstract
Bayesian Ying-Yang (BYY) learning is proposed as a unified statistical learning framework firstly in (Xu, 1995) and systematically developed in past years. Its consists of a general BYY system and a fundamental harmony learning principle as a unified guide for developing new parameter learning algorithms, new regularization techniques, new model selection criteria, as well as a new learning approach that implements parameter learning with model selection made automatically during learning (Xu, 1999ab 2000a&b). This paper goes further beyond the scope of BYY learning, and provides new results and new understandings on harmony learning from perspectives of conventional parametric models, BYY systems and some general properties of information geometry.
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