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Cover: 9781009123235 | Random Matrix Methods for Machine Learning | Romain Couillet (u. a.)
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Random Matrix Methods for Machine Learning
Buch von Romain Couillet (u. a.)
Sprache: Englisch

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Beschreibung
"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"--
"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"--
Über den Autor
Romain Couillet is a Full Professor at Grenoble-Alpes University, France. Prior to that, he was a Full Professor at CentraleSupélec, University of Paris-Saclay. His research topics are in random matrix theory applied to statistics, machine learning, and signal processing. He is the recipient of the 2021 IEEE/SEE Glavieux prize, of the 2013 CNRS Bronze Medal, and of the 2013 IEEE ComSoc Outstanding Young Researcher Award.
Inhaltsverzeichnis
Preface; 1. Introduction; 2. Random matrix theory; 3. Statistical inference in Linear Models; 4. Kernel methods; 5. Large neural networks; 6. Large dimensional convex optimization; 7. Community detection on graphs; 8. Universality and real data; Bibliography; Index.
Details
Erscheinungsjahr: 2022
Genre: Informatik
Rubrik: Naturwissenschaften & Technik
Medium: Buch
Seiten: 408
Inhalt: Gebunden
ISBN-13: 9781009123235
ISBN-10: 1009123238
Sprache: Englisch
Einband: Gebunden
Autor: Couillet, Romain
Liao, Zhenyu
Hersteller: Cambridge University Press
Maße: 247 x 173 x 24 mm
Von/Mit: Romain Couillet (u. a.)
Erscheinungsdatum: 21.07.2022
Gewicht: 0,89 kg
preigu-id: 121612521
Über den Autor
Romain Couillet is a Full Professor at Grenoble-Alpes University, France. Prior to that, he was a Full Professor at CentraleSupélec, University of Paris-Saclay. His research topics are in random matrix theory applied to statistics, machine learning, and signal processing. He is the recipient of the 2021 IEEE/SEE Glavieux prize, of the 2013 CNRS Bronze Medal, and of the 2013 IEEE ComSoc Outstanding Young Researcher Award.
Inhaltsverzeichnis
Preface; 1. Introduction; 2. Random matrix theory; 3. Statistical inference in Linear Models; 4. Kernel methods; 5. Large neural networks; 6. Large dimensional convex optimization; 7. Community detection on graphs; 8. Universality and real data; Bibliography; Index.
Details
Erscheinungsjahr: 2022
Genre: Informatik
Rubrik: Naturwissenschaften & Technik
Medium: Buch
Seiten: 408
Inhalt: Gebunden
ISBN-13: 9781009123235
ISBN-10: 1009123238
Sprache: Englisch
Einband: Gebunden
Autor: Couillet, Romain
Liao, Zhenyu
Hersteller: Cambridge University Press
Maße: 247 x 173 x 24 mm
Von/Mit: Romain Couillet (u. a.)
Erscheinungsdatum: 21.07.2022
Gewicht: 0,89 kg
preigu-id: 121612521
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