A unified perspective and new results on RHT computing, mixture based learning, and multi-learner based problem solving
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...
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| Veröffentlicht in: | Pattern recognition Jg. 40; H. 8; S. 2129 - 2153 |
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| Format: | Journal Article |
| Sprache: | Englisch |
| Veröffentlicht: |
Elsevier Ltd
01.08.2007
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| ISSN: | 0031-3203, 1873-5142 |
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| Abstract | 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. |
|---|---|
| AbstractList | 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. |
| Author | Xu, Lei |
| Author_xml | – sequence: 1 givenname: Lei surname: Xu fullname: Xu, Lei email: lxu@cse.cuhk.edu.hk organization: Department of Computer Science and Engineering, Chinese University of Hong Kong, Hong Kong |
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| Keywords | Multi-sets modelling Evidence combination Mixture of experts Bayesian Ying–Yang learning RBF nets Automatic model selection Object detection Elliptic RPCL Local subspaces Hough transform Rival penalized competitive learning (RPCL) |
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| SubjectTerms | Automatic model selection Bayesian Ying–Yang learning Elliptic RPCL Evidence combination Hough transform Local subspaces Mixture of experts Multi-sets modelling Object detection RBF nets Rival penalized competitive learning (RPCL) |
| Title | A unified perspective and new results on RHT computing, mixture based learning, and multi-learner based problem solving |
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