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Markov Models For Pattern Recognition: From Theory To Applications

This comprehensive introduction to the Markov modeling framework describes the underlying theoretical concepts of Markov models as used for sequential data, covering Hidden Markov models and Markov chain models. It also presents the techniques necessary to build successful systems for practical applications. In addition, the book demonstrates the actual use of the technology in the three main application areas of pattern recognition methods based on Markov-Models: speech recognition, handwriting recognition, and biological sequence analysis. The book is suitable for experts as well as for practitioners.

Hardcover: 248 pages

Publisher: Springer; 2008 edition (October 2, 2007)

Language: English

ISBN-10: 3540717668

ISBN-13: 978-3540717669

Product Dimensions: 6.9 x 0.8 x 9.4 inches

Shipping Weight: 1.3 pounds

Average Customer Review: 4.2 out of 5 stars  See all reviews (4 customer reviews)

Best Sellers Rank: #3,093,573 in Books (See Top 100 in Books) #74 in Books > Computers & Technology > Software > Voice Recognition #150 in Books > Self-Help > Handwriting Analysis #229 in Books > Computers & Technology > Computer Science > AI & Machine Learning > Natural Language Processing

Excellent book, can't say enough good things about it. The coverage is well thought out and the explanations are extremely thorough. Sure, there are some downsides: it's definitely written in 'German English' (as others have mentioned), and the Bioinformatics references seem to gloss over dropouts (out of scope for the book, but should be mentioned all the same). The problems are minor, and it's a great reference/reason for expanding your research into other areas if breadth and depth are your goals.

Though, at times, it shows that the author is German (I mean, in the construction of statements), and the use of commas is a bit slack, I'm very impressed by the clarity (and also, by the practicality) of the exposition. I have not finished the book yet, but nearing the first half of it, almost every page teaches me, of clarifies me, some useful concept. I've tried my hand at Hidden Markov Models with some classical papers (e.g. Rabiner) and books (e.g. MacDonald and Zucchini) ... I wish I had started here.

I am an engineer and I read the book. It was a fairly quick read and I learned a lot. I used some of the algorithms for work. I recommend the book. Though I had some knowledge of HMM, this could be a good introduction.

Even if you don't speak German.Kidding aside, the language in this book is convoluted, obtuse and obviously German lightly translated. I am still working on the introductory statistics section, but I find it easier to read about the concepts mentioned elsewhere online, like on wikipedia. The issues are confused, unnecessarily general, pedantic and overly mathematical. I am a very good programmer but not super mathematical so a more algorithmic approach to the subject would have been better for me. I'll update my review when I get the applied part of the book, but the applied sections don't seem to let up on the mathematical notation. Pseudo code or C or any kind of algorithmic description would have been appreciated.

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