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Titlebook: Markov Models for Pattern Recognition; From Theory to Appli Gernot A. Fink Book 20081st edition Springer-Verlag Berlin Heidelberg 2008 Biol

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樓主
發(fā)表于 2025-3-21 19:53:11 | 只看該作者 |倒序瀏覽 |閱讀模式
書目名稱Markov Models for Pattern Recognition
副標題From Theory to Appli
編輯Gernot A. Fink
視頻videohttp://file.papertrans.cn/625/624632/624632.mp4
概述Covers speech, handwriting recognition, and biological sequence analysis.Both theoretical foundations as well as methods required to build successful applications in practice are presented.Suitable fo
圖書封面Titlebook: Markov Models for Pattern Recognition; From Theory to Appli Gernot A. Fink Book 20081st edition Springer-Verlag Berlin Heidelberg 2008 Biol
描述.Markov models are used to solve challenging pattern recognition problems on the basis of sequential data as, e.g., automatic speech or handwriting recognition. This comprehensive introduction to the Markov modeling framework describes both the underlying theoretical concepts of Markov models - covering Hidden Markov models and Markov chain models - as used for sequential data and presents the techniques necessary to build successful systems for practical applications...Additionally, the actual use of the technology in the three main application areas of pattern recognition methods based on Markov- Models - namely speech recognition, handwriting recognition, and biological sequence analysis -?are demonstrated..
出版日期Book 20081st edition
關鍵詞Biological Sequence Analysis; Hidden Markov Model; Markov Model; Markov-Models; Pattern Recognition; algo
版次1
doihttps://doi.org/10.1007/978-3-540-71770-6
copyrightSpringer-Verlag Berlin Heidelberg 2008
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Integrated Search Methodso keep the search effort manageable. The description of such restrictions by .-gram models offers the decisive advantage over other methods, that two compatible formalisms are used and thus a combined application is possible more easily and with greater success.
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發(fā)表于 2025-3-22 01:06:16 | 只看該作者
Character and Handwriting Recognitionexists, that rely on the classic distinction between segmentation and classification. Segmentation-free methods on the basis of Markov models, however, are mainly applied by researchers, which previously gathered experiences with this technology in the field of automatic speech recognition.
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Introductionachines developed thus should take over tasks, which of course could be carried out by humans, too. However, these tasks could be performed with substantially more perseverance by an automatic system and thus more reliably and consequently also more cheaply.
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Book 20081st editionpractical applications...Additionally, the actual use of the technology in the three main application areas of pattern recognition methods based on Markov- Models - namely speech recognition, handwriting recognition, and biological sequence analysis -?are demonstrated..
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