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Advances in kernel methods:

support vector learning
Frontcover
Bernhard Schölkopf, Christopher J. C.. Burges, Alexander J.. Smola
3 Rezensionen
MIT Press, 1999 - 376 Seiten
The Support Vector Machine is a powerful new learning algorithm for solving a variety of learning and function estimation problems, such as pattern recognition, regression estimation, and operator inversion. The impetus for this collection was a workshop on Support Vector Machines held at the 1997 NIPS conference. The contributors, both university researchers and engineers developing applications for the corporate world, form a Who's Who of this exciting new area.

Contributors: Peter Bartlett, Kristin P. Bennett, Christopher J. C. Burges, Nello Cristianini, Alex Gammerman, Federico Girosi, Simon Haykin, Thorsten Joachims, Linda Kaufman, Jens Kohlmorgen, Ulrich Kressel, Davide Mattera, Klaus-Robert Muller, Manfred Opper, Edgar E. Osuna, John C. Platt, Gunnar Ratsch, Bernhard Scholkopf, John Shawe-Taylor, Alexander J. Smola, Mark O. Stitson, Vladimir Vapnik, Volodya Vovk, Grace Wahba, Chris Watkins, Jason Weston, Robert C. Williamson.
  

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Inhalt

Roadmap
17
Three Remarks on the Support Vector Method of Function
25
Generalization Performance of Support Vector Machines
43
Bayesian Voting Schemes and Large Margin Classifiers
55
Support Vector Machines Reproducing Kernel Hilbert Spaces
69
Geometry and Invariance in Kernel Based Methods
89
Entropy Numbers Operators and Support Vector Kernels
127
Vector Classification
147
Support Vector Machines for Dynamic Reconstruction of
211
Using Support Vector Machines for Time Series Prediction
243
Pairwise Classification and Support Vector Machines
255
Reducing the Runtime Complexity in Support Vector Machines
271
Support Vector Regression with ANOVA Decomposition Kernels
285
Support Vector Density Estimation
293
Combining Support Vector and Mathematical Programming
307
Kernel Principal Component Analysis
327

Making LargeScale Support Vector Machine Learning Practical
169
Fast Training of Support Vector Machines Using Sequential
185
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Advances in kernel methods: support vector learning
Advances in kernel methods: support vector learning
Bernhard Schölkopf,Christopher J. C.. Burges,Alexander J. Smola
Keine Leseprobe verfügbar - 1999

Häufige Begriffe und Wortgruppen

algorithm ANOVA approach approximation approximation error benchmark bound chapter choice choose classifiers coefficients compute conjugate gradient consider constraints construct corresponding covering numbers CPU sec data set decision function decision tree decomposition defined density estimation dimensional distribution dot product e-insensitive eigenvalues eigenvectors entropy entropy numbers equation error fc(x feature space figure Gaussian given heuristic Hilbert space hyperplane input space invariant iteration kernel function kernel PCA Lagrange multipliers learning linear program loss function mapping margin matrix Mercer kernel method metric minimize multicategory nonlinear nonzero number of support obtained optimal hyperplane optimization problem parameters pattern recognition PCG chunking performance polynomial kernels principal components QP problem quadratic programming random reconstruction samples Scholkopf Smola solution solve sparse subset support vector machine SV machines SVM pairwise technique test set Theorem training data training examples training set Vapnik variables VC dimension Wahba zero

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Referenzen von Webseiten

Advances in kernel methods : support vector learning [worldcat.org]
Advances in kernel methods : support vector learning. By: Bernhard Schölkopf; Christopher jc Burges; Alexander J Smola. Type: English : Book ...
worldcat.org/ isbn/ 9780262194167

Advances in Kernel Methods - Support Vector Learning - Sch, Burges ...
This paper should not be used as an indication of the quality of the method. The primary weakness of the MPM approaches is that they have not been guided by ...
citeseer.ist.psu.edu/ 33565.html

Advances in Kernel Methods
1998/08/25 16:31. Advances in Kernel Methods. Support Vector Learning. edited by. Bernhard Scholkopf. Christopher jc Burges. Alexander J. Smola ...
www.rpi.edu/ ~bennek/ svmbook.ps

New Support Vector Algorithms -- Schölkopf et al. 12 (5): 1207 ...
This Article. Right arrow, Abstract Freely available. Right arrow, Full Text (PDF). Right arrow, Alert me when this article is cited. Right arrow ...
neco.mitpress.org/ cgi/ content/ full/ 12/ 5/ 1207

Advances in kernel methods
Advances in kernel methods: support vector learning. Purchase this Divisible Book · Purchase this Divisible Book. Source. Advances in kernel methods table ...
portal.acm.org/ citation.cfm?id=299094

Books: Advances in Kernel Methods
MIT cognet, The Brain Sciences Connection · The MIT Press, Link to Online Catalog · SPARC Communities. Subscriber » LOG IN ...
cognet.mit.edu/ library/ books/ view?isbn=0262194163

A Support Vector Machine with a Hybrid Kernel and Minimal Vapnik ...
[18] T. Joachims, Making Large-Scale SVM Learning Practical Advances in Kernel Methods Support Vector Learning, B. Scholkopf, cjc Burges, and aj Smola, ...
csdl.computer.org/ comp/ trans/ tk/ 2004/ 04/ k0385abs.htm

Kermit related external resources
Resources on the WWW. The following is a list of pointers to websites with useful Kernel Method related resources:. General Sites on Kernel Methods and svms ...
www.euro-kermit.org/ Resources.htm

Top 100 SVM Publications
year); JOACHIMS, T., 1999. Making large-scale support vector machine learning practical. Advances in kernel methods: support vector learning table of … ...
www.svms.org/ top100.html

An extrapolated sequential minimal optimization algorithm for ...
An extrapolated Sequential Minimal Optimization Algorithm for. Support Vector Machines. *D.Lai. ,. *N.Mani, +M.Palaniswami ...
ieeexplore.ieee.org/ iel5/ 9048/ 28701/ 01287693.pdf?tp=& isnumber=& arnumber=1287693

Über den Autor (1999)

Bernhard Scholkopf is Managing Director of the Max Planck Institute for Biological Cybernetics in Tubingen, Germany. He is coauthor of "Learning with Kernels" (MIT Press, 2002) and is a coeditor of "Advances in Kernel Methods: Support Vector Learning" (1998), "Advances in Large-Margin Classifiers" (2000), and "Kernel Methods in Computational Biology" (2004), all published by The MIT Press

Burges is Distinguished Member of Technical Staff at Lucent Technologies, Bell Laboratories.

Alexander J. Smola is Senior Principal Researcher and Machine Learning Program Leader at National ICT Australia/Australian National University, Canberra.

Bibliografische Informationen

QR code for Advances in kernel methods
TitelAdvances in kernel methods: support vector learning
HerausgeberBernhard Schölkopf, Christopher J. C.. Burges, Alexander J.. Smola
Ausgabeillustriert
VerlagMIT Press, 1999
ISBN0262194163, 9780262194167
Länge376 Seiten
  
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