About: Cutting-Plane Methods in Machine Learning     Goto   Sponge   NotDistinct   Permalink

An Entity of Type : http://linked.opendata.cz/ontology/domain/vavai/Vysledek, within Data Space : linked.opendata.cz associated with source document(s)

AttributesValues
rdf:type
Description
  • Cutting plane methods are optimization techniques that incrementally construct an approximation of a feasible set or an objective function by linear inequalities, called cutting planes. Numerous variants of this basic idea are among standard tools used in convex nonsmooth optimization and integer linear programing. Recently, cutting plane methods have seen growing interest in the field of machine learning. In this chapter, we describe the basic theory behind these methods and we show three of their successful applications to solving machine learning problems: regularized risk minimization, multiple kernel learning, and MAP inference in graphical models.
  • Cutting plane methods are optimization techniques that incrementally construct an approximation of a feasible set or an objective function by linear inequalities, called cutting planes. Numerous variants of this basic idea are among standard tools used in convex nonsmooth optimization and integer linear programing. Recently, cutting plane methods have seen growing interest in the field of machine learning. In this chapter, we describe the basic theory behind these methods and we show three of their successful applications to solving machine learning problems: regularized risk minimization, multiple kernel learning, and MAP inference in graphical models. (en)
Title
  • Cutting-Plane Methods in Machine Learning
  • Cutting-Plane Methods in Machine Learning (en)
skos:prefLabel
  • Cutting-Plane Methods in Machine Learning
  • Cutting-Plane Methods in Machine Learning (en)
skos:notation
  • RIV/68407700:21230/12:00193285!RIV13-MSM-21230___
http://linked.open...avai/predkladatel
http://linked.open...avai/riv/aktivita
http://linked.open...avai/riv/aktivity
  • P(1M0567), Z(MSM6840770038)
http://linked.open...vai/riv/dodaniDat
http://linked.open...aciTvurceVysledku
http://linked.open.../riv/druhVysledku
http://linked.open...iv/duvernostUdaju
http://linked.open...titaPredkladatele
http://linked.open...dnocenehoVysledku
  • 129318
http://linked.open...ai/riv/idVysledku
  • RIV/68407700:21230/12:00193285
http://linked.open...riv/jazykVysledku
http://linked.open.../riv/klicovaSlova
  • cutting plane algorithm; Bundle methods; Multiple kernel learning; MAP inference in graphical models (en)
http://linked.open.../riv/klicoveSlovo
http://linked.open...ontrolniKodProRIV
  • [CE651F4F1A51]
http://linked.open...i/riv/mistoVydani
  • Cambridge
http://linked.open...vEdiceCisloSvazku
  • Neural Information Processing č. sv.
http://linked.open...i/riv/nazevZdroje
  • Optimization for Machine Learning
http://linked.open...in/vavai/riv/obor
http://linked.open...ichTvurcuVysledku
http://linked.open...v/pocetStranKnihy
http://linked.open...cetTvurcuVysledku
http://linked.open...vavai/riv/projekt
http://linked.open...UplatneniVysledku
http://linked.open...iv/tvurceVysledku
  • Werner, Tomáš
  • Franc, Vojtěch
  • Sonnenburg, S.
http://linked.open...n/vavai/riv/zamer
number of pages
http://purl.org/ne...btex#hasPublisher
  • The MIT Press
https://schema.org/isbn
  • 978-0-262-01646-9
http://localhost/t...ganizacniJednotka
  • 21230
is http://linked.open...avai/riv/vysledek of
Faceted Search & Find service v1.16.118 as of Jun 21 2024


Alternative Linked Data Documents: ODE     Content Formats:   [cxml] [csv]     RDF   [text] [turtle] [ld+json] [rdf+json] [rdf+xml]     ODATA   [atom+xml] [odata+json]     Microdata   [microdata+json] [html]    About   
This material is Open Knowledge   W3C Semantic Web Technology [RDF Data] Valid XHTML + RDFa
OpenLink Virtuoso version 07.20.3240 as of Jun 21 2024, on Linux (x86_64-pc-linux-gnu), Single-Server Edition (126 GB total memory, 112 GB memory in use)
Data on this page belongs to its respective rights holders.
Virtuoso Faceted Browser Copyright © 2009-2024 OpenLink Software