Kernel methods in computational biology / (Record no. 72986)
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| 000 -LEADER | |
|---|---|
| fixed length control field | 03330nam a2200553 i 4500 |
| 001 - CONTROL NUMBER | |
| control field | 6267331 |
| 005 - DATE AND TIME OF LATEST TRANSACTION | |
| control field | 20220712204632.0 |
| 008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION | |
| fixed length control field | 151229s2004 maua ob 001 eng d |
| 020 ## - INTERNATIONAL STANDARD BOOK NUMBER | |
| ISBN | 9780262256926 |
| -- | electronic |
| 020 ## - INTERNATIONAL STANDARD BOOK NUMBER | |
| -- | |
| 020 ## - INTERNATIONAL STANDARD BOOK NUMBER | |
| -- | alk. paper |
| 082 00 - CLASSIFICATION NUMBER | |
| Call Number | 570/.285 |
| 245 00 - TITLE STATEMENT | |
| Title | Kernel methods in computational biology / |
| 300 ## - PHYSICAL DESCRIPTION | |
| Number of Pages | 1 PDF (ix, 400 pages) : |
| 490 1# - SERIES STATEMENT | |
| Series statement | Computational molecular biology |
| 490 1# - SERIES STATEMENT | |
| Series statement | Computational biology |
| 500 ## - GENERAL NOTE | |
| Remark 1 | "A Bradford book." |
| 520 ## - SUMMARY, ETC. | |
| Summary, etc | Modern 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. |
| 700 1# - AUTHOR 2 | |
| Author 2 | Sch�olkopf, Bernhard. |
| 700 1# - AUTHOR 2 | |
| Author 2 | Tsuda, Koji. |
| 700 1# - AUTHOR 2 | |
| Author 2 | Vert, Jean-Philippe. |
| 856 42 - ELECTRONIC LOCATION AND ACCESS | |
| Uniform Resource Identifier | https://ieeexplore.ieee.org/xpl/bkabstractplus.jsp?bkn=6267331 |
| 942 ## - ADDED ENTRY ELEMENTS (KOHA) | |
| Koha item type | eBooks |
| 264 #1 - | |
| -- | Cambridge, Massachusetts : |
| -- | MIT Press, |
| -- | c2004. |
| 264 #2 - | |
| -- | [Piscataqay, New Jersey] : |
| -- | IEEE Xplore, |
| -- | [2004] |
| 336 ## - | |
| -- | text |
| -- | rdacontent |
| 337 ## - | |
| -- | electronic |
| -- | isbdmedia |
| 338 ## - | |
| -- | online resource |
| -- | rdacarrier |
| 588 ## - | |
| -- | Description based on PDF viewed 12/29/2015. |
| 650 #0 - SUBJECT ADDED ENTRY--SUBJECT 1 | |
| -- | Computational biology. |
| 650 #0 - SUBJECT ADDED ENTRY--SUBJECT 1 | |
| -- | Kernel functions. |
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