LEADER 03597nam 22005895 450 001 9910300246703321 005 20220120123340.0 010 $a3-319-25388-3 024 7$a10.1007/978-3-319-25388-6 035 $a(MiAaPQ)EBC4188208 035 $a(DE-He213)978-3-319-25388-6 035 $a(PPN)190884444 035 $a(EXLCZ)993710000000541903 100 $a20151208d2015 u| 0 101 0 $aeng 135 $aur||||||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aLectures on the Nearest Neighbor Method$b[electronic resource] /$fby Gérard Biau, Luc Devroye 205 $a1st ed. 2015. 210 1$aCham :$cSpringer International Publishing :$cImprint: Springer,$d2015. 215 $aIX, 290 p. ; $cil. en col 225 1 $aSpringer Series in the Data Sciences,$x2365-5674 300 $aMSC 68Wxx ; 60Exx ; 62Exx ; 68T10 320 $aIncludes bibliographical references and index. 327 $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. 330 $aThis text presents a wide-ranging and rigorous overview of nearest neighbor methods, one of the most important paradigms in machine learning. Now in one self-contained volume, this book systematically covers key statistical, probabilistic, combinatorial and geometric ideas for understanding, analyzing and developing nearest neighbor methods. Gérard Biau is a professor at Université Pierre et Marie Curie (Paris). Luc Devroye is a professor at the School of Computer Science at McGill University (Montreal).   . 410 0$aSpringer Series in the Data Sciences,$x2365-5674 606 $aProbabilities 606 $aPattern recognition 606 $aStatistics  606 $aProbability Theory and Stochastic Processes$3https://scigraph.springernature.com/ontologies/product-market-codes/M27004 606 $aPattern Recognition$3https://scigraph.springernature.com/ontologies/product-market-codes/I2203X 606 $aStatistics and Computing/Statistics Programs$3https://scigraph.springernature.com/ontologies/product-market-codes/S12008 615 0$aProbabilities. 615 0$aPattern recognition. 615 0$aStatistics . 615 14$aProbability Theory and Stochastic Processes. 615 24$aPattern Recognition. 615 24$aStatistics and Computing/Statistics Programs. 676 $a510 700 $aBiau$b Gérard$4aut$4http://id.loc.gov/vocabulary/relators/aut$0755683 702 $aDevroye$b Luc$4aut$4http://id.loc.gov/vocabulary/relators/aut 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 856 4 $ahttps://link.springer.com/book/10.1007/978-3-319-25388-6 906 $aBOOK 912 $a9910300246703321 996 $aLectures on the Nearest Neighbor Method$92545885 997 $aUNINA