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007 | cr nn 008mamaa | ||
008 | 170213s2015 gw | s |||| 0|eng d | ||
020 |
_a9783319253886 _9978-3-319-25388-6 |
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024 | 7 |
_a10.1007/978-3-319-25388-6 _2doi |
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035 | _ato000560951 | ||
040 |
_aSpringer _cSpringer _dRU-ToGU |
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050 | 4 | _aQA273.A1-274.9 | |
050 | 4 | _aQA274-274.9 | |
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_aPBT _2bicssc |
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_aPBWL _2bicssc |
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_aMAT029000 _2bisacsh |
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082 | 0 | 4 |
_a519.2 _223 |
100 | 1 |
_aBiau, Gérard. _eauthor. _9467717 |
|
245 | 1 | 0 |
_aLectures on the Nearest Neighbor Method _helectronic resource _cby Gérard Biau, Luc Devroye. |
250 | _a1st ed. 2015. | ||
260 |
_aCham : _bSpringer International Publishing : _bImprint: Springer, _c2015. |
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300 |
_aIX, 290 p. 4 illus. in color. _bonline resource. |
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336 |
_atext _btxt _2rdacontent |
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337 |
_acomputer _bc _2rdamedia |
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338 |
_aonline resource _bcr _2rdacarrier |
||
490 | 1 |
_aSpringer Series in the Data Sciences, _x2365-5674 |
|
505 | 0 | _aPart I: Density Estimation -- Order Statistics and Nearest Neighbors -- The Expected Nearest Neighbor Distance -- The k-nearest Neighbor Density Estimate -- Uniform Consistency -- Weighted k-nearest neighbor density estimates.- Local Behavior -- Entropy Estimation -- Part II: Regression Estimation -- The Nearest Neighbor Regression Function Estimate -- The 1-nearest Neighbor Regression Function Estimate -- LP-consistency and Stone's Theorem -- Pointwise Consistency -- Uniform Consistency -- Advanced Properties of Uniform Order Statistics -- Rates of Convergence -- Regression: The Noisless Case -- The Choice of a Nearest Neighbor Estimate -- Part III: Supervised Classification -- Basics of Classification -- The 1-nearest Neighbor Classification Rule -- The Nearest Neighbor Classification Rule. Appendix -- Index. | |
520 | _aThis text presents a wide-ranging and rigorous overview of nearest neighbor methods, one of the most important paradigms in machine learning. Now in one self-contained volume, this book systematically covers key statistical, probabilistic, combinatorial and geometric ideas for understanding, analyzing and developing nearest neighbor methods. Gérard Biau is a professor at Université Pierre et Marie Curie (Paris). Luc Devroye is a professor at the School of Computer Science at McGill University (Montreal). . | ||
650 | 0 |
_amathematics. _9566183 |
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650 | 0 |
_aPattern Recognition. _9304129 |
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650 | 0 |
_aProbabilities. _9295556 |
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650 | 0 |
_aStatistics. _9124796 |
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650 | 1 | 4 |
_aMathematics. _9566184 |
650 | 2 | 4 |
_aProbability Theory and Stochastic Processes. _9303734 |
650 | 2 | 4 |
_aPattern Recognition. _9304129 |
650 | 2 | 4 |
_aStatistics and Computing/Statistics Programs. _9303277 |
700 | 1 |
_aDevroye, Luc. _eauthor. _9467718 |
|
710 | 2 |
_aSpringerLink (Online service) _9143950 |
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773 | 0 | _tSpringer eBooks | |
830 | 0 |
_aSpringer Series in the Data Sciences, _9467719 |
|
856 | 4 | 0 | _uhttp://dx.doi.org/10.1007/978-3-319-25388-6 |
912 | _aZDB-2-SMA | ||
999 | _c415335 |