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Learning kernel classifiers
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ISBN: 1282096389 0262256339 9786612096389 0585436681 026208306X 9780262256339 9780585436685 9780262083065 0262263041 Year: 2002 Publisher: Cambridge, Mass. MIT Press

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Abstract

Linear classifiers in kernel spaces have emerged as a major topic within the field of machine learning. The kernel technique takes the linear classifier--a limited, but well-established and comprehensively studied model--and extends its applicability to a wide range of nonlinear pattern-recognition tasks such as natural language processing, machine vision, and biological sequence analysis. This book provides the first comprehensive overview of both the theory and algorithms of kernel classifiers, including the most recent developments. It begins by describing the major algorithmic advances: kernel perceptron learning, kernel Fisher discriminants, support vector machines, relevance vector machines, Gaussian processes, and Bayes point machines. Then follows a detailed introduction to learning theory, including VC and PAC-Bayesian theory, data-dependent structural risk minimization, and compression bounds. Throughout, the book emphasizes the interaction between theory and algorithms: how learning algorithms work and why. The book includes many examples, complete pseudo code of the algorithms presented, and an extensive source code library.


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The NeurIPS '18 Competition : From Machine Learning to Intelligent Conversations
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ISBN: 3030291359 3030291340 Year: 2020 Publisher: Cham : Springer International Publishing : Imprint: Springer,

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This volume presents the results of the Neural Information Processing Systems Competition track at the 2018 NeurIPS conference. The competition follows the same format as the 2017 competition track for NIPS. Out of 21 submitted proposals, eight competition proposals were selected, spanning the area of Robotics, Health, Computer Vision, Natural Language Processing, Systems and Physics. Competitions have become an integral part of advancing state-of-the-art in artificial intelligence (AI). They exhibit one important difference to benchmarks: Competitions test a system end-to-end rather than evaluating only a single component; they assess the practicability of an algorithmic solution in addition to assessing feasibility.


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F♯ for machine learning essentials
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ISBN: 1783989351 9781783989355 9781783989348 1783989343 Year: 2016 Publisher: Birmingham, England Mumbai [India] Packt Publishing

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The NeurIPS '18 Competition : From Machine Learning to Intelligent Conversations
Authors: ---
ISBN: 9783030291358 Year: 2020 Publisher: Cham Springer International Publishing

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Abstract

This volume presents the results of the Neural Information Processing Systems Competition track at the 2018 NeurIPS conference. The competition follows the same format as the 2017 competition track for NIPS. Out of 21 submitted proposals, eight competition proposals were selected, spanning the area of Robotics, Health, Computer Vision, Natural Language Processing, Systems and Physics. Competitions have become an integral part of advancing state-of-the-art in artificial intelligence (AI). They exhibit one important difference to benchmarks: Competitions test a system end-to-end rather than evaluating only a single component; they assess the practicability of an algorithmic solution in addition to assessing feasibility.


Book
The NeurIPS '18 Competition
Authors: --- ---
ISBN: 9783030291358 Year: 2020 Publisher: Cham Springer International Publishing :Imprint: Springer

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