| 000 | 03330nam a2200553 i 4500 | ||
|---|---|---|---|
| 001 | 6267331 | ||
| 003 | IEEE | ||
| 005 | 20220712204632.0 | ||
| 006 | m o d | ||
| 007 | cr |n||||||||| | ||
| 008 | 151229s2004 maua ob 001 eng d | ||
| 010 | _z 2003068640 (print) | ||
| 020 |
_a9780262256926 _qelectronic |
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| 020 |
_z9780262195096 _qprint |
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| 020 |
_z0262195097 _qalk. paper |
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| 035 | _a(CaBNVSL)mat06267331 | ||
| 035 | _a(IDAMS)0b000064818b42fd | ||
| 040 |
_aCaBNVSL _beng _erda _cCaBNVSL _dCaBNVSL |
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| 050 | 4 |
_aQH324.2 _b.K47 2004eb |
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| 082 | 0 | 0 |
_a570/.285 _222 |
| 245 | 0 | 0 |
_aKernel methods in computational biology / _cedited by Bernhard Sch�olkopf, Koji Tsuda, Jean-Philippe Vert. |
| 264 | 1 |
_aCambridge, Massachusetts : _bMIT Press, _cc2004. |
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| 264 | 2 |
_a[Piscataqay, New Jersey] : _bIEEE Xplore, _c[2004] |
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| 300 |
_a1 PDF (ix, 400 pages) : _billustrations. |
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| 336 |
_atext _2rdacontent |
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| 337 |
_aelectronic _2isbdmedia |
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| 338 |
_aonline resource _2rdacarrier |
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| 490 | 1 | _aComputational molecular biology | |
| 490 | 1 | _aComputational biology | |
| 500 | _a"A Bradford book." | ||
| 504 | _aIncludes bibliographical references (p. [357]-389) and index. | ||
| 506 | 1 | _aRestricted to subscribers or individual electronic text purchasers. | |
| 520 | _aModern machine learning techniques are proving to be extremely valuable for the analysis of data in computational biology problems. One branch of machine learning, kernel methods, lends itself particularly well to the difficult aspects of biological data, which include high dimensionality (as in microarray measurements), representation as discrete and structured data (as in DNA or amino acid sequences), and the need to combine heterogeneous sources of information. This book provides a detailed overview of current research in kernel methods and their applications to computational biology.Following three introductory chapters -- an introduction to molecular and computational biology, a short review of kernel methods that focuses on intuitive concepts rather than technical details, and a detailed survey of recent applications of kernel methods in computational biology -- the book is divided into three sections that reflect three general trends in current research. The first part presents different ideas for the design of kernel functions specifically adapted to various biological data; the second part covers different approaches to learning from heterogeneous data; and the third part offers examples of successful applications of support vector machine methods. | ||
| 530 | _aAlso available in print. | ||
| 538 | _aMode of access: World Wide Web | ||
| 588 | _aDescription based on PDF viewed 12/29/2015. | ||
| 650 | 0 |
_aComputational biology. _915342 |
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| 650 | 0 |
_aKernel functions. _921554 |
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| 655 | 0 |
_aElectronic books. _93294 |
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| 700 | 1 |
_aSch�olkopf, Bernhard. _922180 |
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| 700 | 1 |
_aTsuda, Koji. _922181 |
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| 700 | 1 |
_aVert, Jean-Philippe. _922182 |
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| 710 | 2 |
_aIEEE Xplore (Online Service), _edistributor. _922183 |
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| 710 | 2 |
_aMIT Press, _epublisher. _922184 |
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| 776 | 0 | 8 |
_iPrint version: _z9780262195096 |
| 830 | 0 |
_aComputational molecular biology _922185 |
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| 856 | 4 | 2 |
_3Abstract with links to resource _uhttps://ieeexplore.ieee.org/xpl/bkabstractplus.jsp?bkn=6267331 |
| 942 | _cEBK | ||
| 999 |
_c72986 _d72986 |
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