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This paper further proposes the conjugate and natural gradient rules to efficiently implement the maximization of the harmony function, i.e. the BYY harmony learning, on Gaussian mixture. It is demonstrated by simulation experiments that these two new gradient rules not only work well, but also converge more quickly than the general gradient ones. <\/jats:p>","DOI":"10.1142\/s0218001405004228","type":"journal-article","created":{"date-parts":[[2005,8,15]],"date-time":"2005-08-15T08:12:31Z","timestamp":1124093551000},"page":"701-713","source":"Crossref","is-referenced-by-count":18,"title":["CONJUGATE AND NATURAL GRADIENT RULES FOR BYY HARMONY LEARNING ON GAUSSIAN MIXTURE WITH AUTOMATED MODEL SELECTION"],"prefix":"10.1142","volume":"19","author":[{"given":"JINWEN","family":"MA","sequence":"first","affiliation":[{"name":"Department of Information Science, School of Mathematical Sciences and LMAM, Peking University, Beijing 100871, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"BIN","family":"GAO","sequence":"additional","affiliation":[{"name":"Department of Information Science, School of Mathematical Sciences and LMAM, Peking University, Beijing 100871, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"YANG","family":"WANG","sequence":"additional","affiliation":[{"name":"Department of Information Science, School of Mathematical Sciences and LMAM, Peking University, Beijing 100871, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"QIANSHENG","family":"CHENG","sequence":"additional","affiliation":[{"name":"Department of Information Science, School of Mathematical Sciences and LMAM, Peking University, Beijing 100871, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"219","published-online":{"date-parts":[[2011,11,21]]},"reference":[{"key":"rf1","doi-asserted-by":"crossref","first-page":"716","DOI":"10.1109\/TAC.1974.1100705","volume":"19","author":"Akaike H.","journal-title":"IEEE Trans. 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