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01803aam a22003138i 4500 001 E1DFD8B0E97711ED8437380758ECA4DB 003 SILO 005 20230503010033 008 220316s2023 enk b 001 0 eng 010 $a 2022009569 020 $a 1009123238 020 $a 9781009123235 035 $a (OCoLC)1336461584 040 $a DLC $b eng $e rda $c DLC $d OCLCF $d SILO 042 $a pcc 050 00 $a Q325.5 C69 2023 100 1 $a Couillet, Romain, $d 1983- $e author. 245 10 $a Random matrix methods for machine learning / $c Romain Couillet, Grenoble-Alps University, Zhenyu Liao, Huazhong University of Science and Technology. 264 1 $a Cambridge, United Kingdom ; $b Cambridge University Press, $c 2023. 300 $a pages cm 504 $a Includes bibliographical references and index. 520 $a "Numerous and large dimensional data is now a default setting in modern machine learning (ML). Standard ML algorithms, starting with kernel methods such as support vector machines and graph-based methods like the PageRank algorithm, were however initially designed out of small dimensional intuitions and tend to misbehave, if not completely collapse, when dealing with real-world large datasets. Random matrix theory has recently developed a broad spectrum of tools to help understand this new curse of dimensionality, to help repair or completely recreate the sub-optimal algorithms, and most importantly to provide new intuitions to deal with modern data mining"-- $c Provided by publisher. 650 0 $a Machine learning $x Mathematics. 650 0 $a Matrix analytic methods. 700 1 $a Liao, Zhenyu, $d 1992- $e author. 941 $a 1 952 $l USUX851 $d 20230706015645.0 956 $a http://locator.silo.lib.ia.us/search.cgi?index_0=id&term_0=E1DFD8B0E97711ED8437380758ECA4DB 994 $a C0 $b IWAInitiate Another SILO Locator Search