| 000 | 01895cam a2200397 a 4500 | ||
|---|---|---|---|
| 001 | 9458615 | ||
| 005 | 20170607123423.0 | ||
| 006 | m d | ||
| 007 | cr n | ||
| 008 | 110425s2012 enka sb 001 0 eng d | ||
| 020 | _a9780521192248 | ||
| 035 | _a(WaSeSS)ssj0000612815 | ||
| 040 |
_aDLC _cDLC _dDLC _dWaSeSS |
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| 050 | 4 |
_aQ325.5 _b.S28 2012 |
|
| 082 | 0 | 0 |
_a006.31 _223 _bBEK-S |
| 084 |
_aCOM016000 _2bisacsh |
||
| 100 | _aBekkerman, Ron | ||
| 210 | 1 | 0 | _aScaling up machine learning |
| 245 | 0 | 0 |
_aScaling up machine learning _bparallel and distributed approaches _cedited by Ron Bekkerman, Mikhail Bilenko, John Langford. |
| 260 |
_aNew York : _bCambridge University Press, _c©2012. |
||
| 300 |
_axvi, 475 p. ; _c28 cm. |
||
| 504 | _aIncludes bibliographical references and index. | ||
| 506 | _aLicense restrictions may limit access. | ||
| 520 | _a"This book presents an integrated collection of representative approaches for scaling up machine learning and data mining methods on parallel and distributed computing platforms. Demand for parallelizing learning algorithms is highly task-specific: in some settings it is driven by the enormous dataset sizes, in others by model complexity or by real-time performance requirements. Making task-appropriate algorithm and platform choices for large-scale machine learning requires understanding the benefits, trade-offs, and constraints of the available options"-- | ||
| 650 | 0 | _aMachine learning. | |
| 650 | 0 | _aData mining. | |
| 650 | 0 | _aParallel algorithms. | |
| 650 | 0 | _aParallel programs (Computer programs) | |
| 650 | 7 |
_aCOMPUTERS / Computer Vision & Pattern Recognition. _2bisacsh |
|
| 700 | 1 | _aBekkerman, Ron. | |
| 700 | 1 | _aBilenko, Mikhail | |
| 700 | 1 | _aLangford, John | |
| 773 | 0 | _tCambridge Books Online - Computer Science | |
| 910 | _aLibrary of Congress record | ||
| 942 |
_2ddc _cBK _01 |
||
| 999 |
_c9519 _d9519 |
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