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篇目详细内容 |
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【篇名】 |
On essential topics of BYY harmony learning: Current status, challenging issues, and gene analysis applications |
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【刊名】 |
Frontiers of Electrical and Electronic Engineering |
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【刊名缩写】 |
Front. Electr. Electron. Eng. |
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【ISSN】 |
2095-2732 |
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【EISSN】 |
2095-2740 |
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【DOI】 |
10.1007/s11460-012-0190-2 |
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【出版社】 |
Higher Education Press and Springer-Verlag Berlin
Heidelberg |
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【出版年】 |
2012 |
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【卷期】 |
7
卷1期 |
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【页码】 |
147-196
页,共
50
页 |
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【作者】 |
Lei XU;
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【关键词】 |
Bayesian Ying-Yang (BYY) harmony learning; harmony functional; automatic model selection; Gaussian mixture; hidden Markov model (HMM) gated temporal factor analysis; hierarchical Gaussian mixture; manifold learning; semi-supervised learning; semi-blind learning; genome-wide association; exome sequencing analysis; gene transcriptional regulation |
【摘要】 |
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As a supplementary of [Xu L. Front. Electr. Electron. Eng. China, 2010, 5(3): 281―328], this paper outlines current status of efforts made on Bayesian Ying-Yang (BYY) harmony learning, plus gene analysis applications. At the beginning, a bird’s-eye view is provided via Gaussian mixture in comparison with typical learning algorithms and model selection criteria. Particularly, semi-supervised learning is covered simply via choosing a scalar parameter. Then, essential topics and demanding issues about BYY system design and BYY harmony learning are systematically outlined, with a modern perspective on Yin-Yang viewpoint discussed, another Yang factorization addressed, and coordinations across and within Ying-Yang summarized. The BYY system acts as a unified framework to accommodate unsupervised, supervised, and semi-supervised learning all in one formulation, while the best harmony learning provides novelty and strength to automatic model selection. Also, mathematical formulation of harmony functional has been addressed as a unified scheme for measuring the proximity to be considered in a BYY system, and used as the best choice among others. Moreover, efforts are made on a number of learning tasks, including a mode-switching factor analysis proposed as a semi-blind learning framework for several types of independent factor analysis, a hidden Markov model (HMM) gated temporal factor analysis suggested for modeling stationary temporal dependence, and a two-level hierarchical Gaussian mixture extended to cover semi-supervised learning, as well as a manifold learning modified to facilitate automatic model selection. Finally, studies are applied to the problems of gene analysis, such as genome-wide association, exome sequencing analysis, and gene transcriptional regulation. |
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