A unified perspective and new results on RHT computing, mixture based learning, and multi-learner based problem solving

作者：Xu Lei^*

来源：Pattern Recognition, 2007, 40(8): 2129-2153.

DOI：10.1016/j.patcog.2006.12.016

• object detection
• hough transform
• rival penalized competitive learning (RPCL)
• elliptic RPCL
• local subspaces
• Bayesian Ying-Yang learning
• automatic model selection
• multi-sets modelling
• mixture of experts
• RBF nets
• evidence combination

On one hand, multiple object detection approaches of Hough transform (HT) type and randomized HT type have been extended into an evidence accumulation featured general framework for problem solving, with five key mechanisms elaborated and several extensions of HT and RHT presented. On the other hand, another framework is proposed to integrate typical multi-learner based approaches for problem solving, particularly on Gaussian mixture based data clustering and local subspace learning, multi-sets mixture based object detection and motion estimation, and multi-agent coordinated problem solving. Typical learning algorithms, especially those that base on rival penalized competitive learning (RPCL) and Bayesian Ying-Yang (BYY) learning, are summarized from a unified perspective with new extensions. Furthermore, the two different frameworks are not only examined with one viewed crossly from a perspective of the other, with new insights and extensions, but also further unified into a general problem solving paradigm that consists of five basic mechanisms in terms of acquisition, allocation, amalgamation, admission, and affirmation, or shortly A5 paradigm.

• 出版日期2007-8
• 单位香港大学; 香港中文大学