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001387451 24500 $$aAdvances in large margin classifiers /$$cedited by Alexander J. Smola [and others].
001387451 260__ $$aCambridge, Mass. :$$bMIT Press,$$c©2000.
001387451 300__ $$a1 online resource (vi, 412 pages) :$$billustrations.
001387451 336__ $$atext$$btxt$$2rdacontent
001387451 337__ $$acomputer$$bc$$2rdamedia
001387451 338__ $$aonline resource$$bcr$$2rdacarrier
001387451 4901_ $$aAdvances in neural information processing systems [i.e. Neural information processing series]
001387451 506__ $$aAccess limited to authorized users.
001387451 520__ $$aThe concept of large margins is a unifying principle for the analysis of many different approaches to the classification of data from examples, including boosting, mathematical programming, neural networks, and support vector machines. The fact that it is the margin, or confidence level, of a classification--that is, a scale parameter--rather than a raw training error that matters has become a key tool for dealing with classifiers. This book shows how this idea applies to both the theoretical analysis and the design of algorithms.The book provides an overview of recent developments in large margin classifiers, examines connections with other methods (e.g., Bayesian inference), and identifies strengths and weaknesses of the method, as well as directions for future research. Among the contributors are Manfred Opper, Vladimir Vapnik, and Grace Wahba.
001387451 588__ $$aOCLC-licensed vendor bibliographic record.
001387451 650_0 $$aMachine learning.
001387451 650_0 $$aAlgorithms.
001387451 650_0 $$aKernel functions.
001387451 653__ $$aCOMPUTER SCIENCE/General
001387451 655_0 $$aElectronic books
001387451 7001_ $$aSmola, Alexander J.
001387451 852__ $$bebk
001387451 85640 $$3MIT Press$$uhttps://univsouthin.idm.oclc.org/login?url=https://doi.org/10.7551/mitpress/1113.001.0001?locatt=mode:legacy$$zOnline Access through The MIT Press Direct
001387451 85642 $$3OCLC metadata license agreement$$uhttp://www.oclc.org/content/dam/oclc/forms/terms/vbrl-201703.pdf
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001387451 983__ $$aOnline