"Learning kernel classifiers: theory and algorithms /"

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Book	Author(s)	Year	Publisher
Learning Kernel Classifiers: Theory and Algorithms	Ralf Herbrich	2001	MIT Press
Learning Kernel Classifiers: Theory and Algorithms	Ralf Herbrich	2001	MIT Press,
Learning kernel classifiers: theory and algorithms /	Herbrich, Ralf.	[2002], ©2002.	MIT Press,
Kernel Learning Algorithms for Face Recognition	Jun-Bao Li; Shu-Chuan Chu; Jeng-Shyang Pan	2014	Springer New York
Deep Learning Classifiers with Memristive Networks: Theory and Applications	Alex Pappachen James	1st ed. 2020	Springer International Publishing
Learning Algorithms Theory and Applications: Theory and Applications	S. Lakshmivarahan	1981	Springer New York
Information theory, inference, and learning algorithms	MacKay D J C		Cambridge University Press
Combining Pattern Classifiers: Methods and Algorithms, Second Edition	Ludmila I. Kuncheva	2014	John Wiley & Sons
Learning Theory and Kernel Machines: 16th Annual Conference on Learning Theory and 7th Kernel Workshop, COLT/Kernel 2003, Washington, DC, USA, August 24-27, 2003. Proceedings	Scholkopf B., Warmuth M.K (ed.)	2003	Springer Berlin Heidelberg
Learning Theory and Kernel Machines: 16th Annual Conference on Learning Theory and 7th Kernel Workshop, COLT/Kernel 2003, Washington, DC, USA, August 24-27, 2003. Proceedings	Michael L. Littman (auth.), Bernhard Schölkopf, Manfred K. Warmuth (eds.)	2003	Springer-Verlag Berlin Heidelberg
Information Theory, Inference & Learning Algorithms	David J.C. MacKay	2003	Cambridge University Press
Learning Theory and Kernel Machines: 16th Annual Conference on Computational Learning Theory and 7th Kernel Workshop, COLT/Kernel 2003, Washington, DC, USA, August 24-27, 2003, Proceedings	Bernhard Schölkopf; Manfred K. Warmuth	2003	Springer Berlin Heidelberg
Kernel Methods and Machine Learning	S. Y. Kung	2014-04	Cambridge University Press
Kernel Based Algorithms for Mining Huge Data Sets: Supervised, Semi-supervised, and Unsupervised Learning	Te-Ming Huang; Vojislav Kecman; Ivica Kopriva	2006	Springer Berlin Heidelberg
Understanding Machine Learning: From Theory to Algorithms	Shai Shalev-Shwartz; Shai Ben-David	2014-05	Cambridge University Press
Online Learning: Theory, Algorithms, and Applications - Ph. D thesis	Shai Shalev-Shwartz
Linux Kernel Networking: Implementation and Theory	Rami Rosen	1st ed.	Apress
Mathematical Learning Models — Theory and Algorithms: Proceedings of a Conference	U. Herkenrath; D. Kalin; W. Vogel	1983	Springer New York
Hybrid System Identification: Theory and Algorithms for Learning Switching Models	Fabien Lauer; Gérard Bloch	1st ed. 2019	Springer International Publishing
Learning Algorithms: Theory and Applications in Signal Processing, Control and Communications	P. Mars	1996	Routledge
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Chapter	Author(s)	Book	Book Authors	Year	Publisher
I. Learning Algorithms. 2. Kernel Classifiers from a Machine Learning Perspective. 3. Kernel Classifiers from a Bayesian Perspective		Learning kernel classifiers: theory and algorithms /	Herbrich, Ralf.	[2002], ©2002.	MIT Press,
Linear and Kernel Classifiers	Jong Chul Ye	Geometry of Deep Learning: A Signal Processing Perspective	Jong Chul Ye	1st ed. 2022	Springer Nature Singapore
Kernel Approximation Classifiers		Matlab. Statistics and Machine Learning Toolbox. User's Guide		2021	MathWorks
Learning Fuzzy Classifiers with Evolutionary Algorithms	Mauro L. Beretta; Andrea G. B. Tettamanzi	Soft Computing Applications	Andrea Bonarini; Francesco Masulli; Gabriella Pasi	2003	Physica-Verlag HD
Learning Sparse Kernel Classifiers in the Primal	Zhouyu Fu; Guojun Lu; Kai-Ming Ting; Dengsheng Zhang	Structural, Syntactic, and Statistical Pattern Recognition: Joint IAPR International Workshop, SSPR & SPR 2012, Hiroshima, Japan, November 7-9, 2012, Proceedings	Georgy Gimel´farb; Edwin Hancock; Atsushi Imiya; Arjan Kuijper; Mineichi Kudo; Shinichiro Omachi; Terry Windeatt; Keiji Yamada	2012	Springer Berlin Heidelberg
Kernel Classifiers from a Machine Learning Perspective		Learning Kernel Classifiers: Theory and Algorithms	Ralf Herbrich	2001	MIT Press
Learning Algorithms — theory and practice	Norman L. Biggs	Neural Network Applications: Proceedings of the Second British Neural Network Society Meeting (NCM91), London, October 1991	J. G. Taylor	1992	Springer London
Dictionary Learning: Theory and Algorithms	Jayaraman J. Tiagarajan; Karthikeyan Natesan Ramamurthy; Pavan Turaga; Andreas Spanias	Image Understanding using Sparse Representations	Jayaraman J. Thiagarajan; Karthikeyan Natesan Ramamurthy; Pavan Turaga; Andreas Spanias		Springer International Publishing
An Introduction to Kernel Learning Algorithms	Peter V. Gehler; Bernhard Schölkopf	Kernel Methods For Remote Sensing Data Analysis	Lorenzo Bruzzone; Gustau Camps-Valls	2009	John Wiley & Sons
Learning Bayesian Belief Network Classifiers: Algorithms and System	Jie Cheng; Russell Greiner	Advances in Artificial Intelligence: 14th Biennial Conference of the Canadian Society for Computational Studies of Intelligence, AI 2001 Ottawa, Canada, June 7-9, 2001 Proceedings	Eleni Stroulia; Stan Matwin	2001	Springer Berlin Heidelberg
Training v-Support Vector Classifiers: Theory and Algorithms			MIT Press	2001	MIT Press
2: Kernel Classifiers from a Machine Learning Perspective		Learning Kernel Classifiers: Theory and Algorithms	Ralf Herbrich	2001	MIT Press,
3 - Kernel algorithms		Nonparametric System Identification	Wlodzimierz Greblicki; Miroslaw Pawlak	2008-06	Cambridge University Press
Statistical Learning Theory and Kernel-Based Methods	Chris Aldrich; Lidia Auret	Unsupervised Process Monitoring and Fault Diagnosis with Machine Learning Methods	Chris Aldrich; Lidia Auret	2013	Springer London
An Introduction to Kernel-Based Learning Algorithms	Klaus-Robert Müller; Sebastian Mika; Koji Tsuda; Koji Schölkopf	Handbook of Neural Network Signal Processing	Jose C. Principe; Yu Hen Hu; Richard C. Dorf; Alexander D. Poularikas; Ling Guan; Jenq-Neng Hwang; Scott C. Douglas; Shigeru Katagiri; Tulay Adali; Michael T. Manry; Klaus-Robert - Muller; Volker Tresp; Konstantinos I. Diamantaras; Jinshiuh Taur; Lizhong Wu; Paisarn Muneesawang; S.Y. Kung; Hau San Wong; Jose A. Lay; Yue Wang; Andrew Back; Shang Hung Lin; Yuangsong Liao; Hema Chandrasekaran; Cheng-Hsiung Hsieh; John Moody; Sebastian Mika; Gunnar Ratsch; Koji Tsuda; Bernhard Scholkopf	2002	Routledge
Discriminative Direction for Kernel Classifiers		Advances in Neural Information Processing Systems 14: Proceedings of the 2001 Conference	Thomas G. Dietterich; Suzanna Becker; Zoubin Ghahramani	2002	MIT Press,
Resilient Approximation of Kernel Classifiers	Thorsten Suttorp; Christian Igel	Artificial Neural Networks - ICANN 2007: 17th International Conference, Porto, Portugal, September 9-13, 2007, Proceedings, Part I	Joaquim Marques de Sá; Luis A. Alexandre; Wlodzislaw Duch; Danilo Mandic	2007	Springer Berlin Heidelberg
Kernel theory		Machine Learning A First Course for Engineers and Scientists [draft]	Andreas Lindholm, Niklas Wahlström, Fredrik Lindsten, Thomas B. Schön	2021
Machine Learning Classifiers	Rachna Behl; Indu Kashyap	Big Data, IoT, and Machine Learning: Tools and Applications	Rashmi Agrawal; Marcin Paprzycki; Neha Gupta	2020	Routledge
Learning Based Classifiers	Anil Kumar; Priyadarshi Upadhyay; A. Senthil Kumar	Fuzzy Machine Learning Algorithms for Remote Sensing Image Classification	Anil Kumar; Priyadarshi Upadhyay; A. Senthil Kumar	2020	Routledge
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Showing 1 to 20 of 100 entries

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