Advances in Kernel Methods: Support Vector LearningBernhard Schölkopf, Christopher J. C. Burges, Alexander J. Smola MIT Press, 1999 - 376 lappuses 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.ContributorsPeter 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 Müller, Manfred Opper, Edgar E. Osuna, John C. Platt, Gunnar Rätsch, Bernhard Schölkopf, 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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14. lappuse
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18. lappuse
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Saturs
vi | 12 |
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 | 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 |
Fast Training of Support Vector Machines Using Sequential | 185 |
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 |
Vector Classification | 147 |
Making LargeScale Support Vector Machine Learning Practical | 169 |
Kernel Principal Component Analysis | 327 |
Bieži izmantoti vārdi un frāzes
algorithm approach approximation Bayesian bound Burges chapter choice classifiers coefficients compute conjugate gradient consider constraints construct corresponding data set decision function defined density estimation dimensional distribution dot product eigenvalues eigenvectors entropy entropy numbers equation error feature space Gaussian given Hilbert space hyperplane hypothesis space input space iteration kernel function kernel PCA Kullback-Leibler distance Lagrange multipliers learning linear loss function mapping margin matrix Mercer kernel method metric minimize nonlinear number of support obtained optimal hyperplane optimization problem pairwise parameters pattern recognition PCG chunking perceptron performance pixel points polynomial kernels principal components quadratic programming random regression Riemannian metric RKHS sample satisfy Schölkopf Smola solution solve sparse subset Support Vector Machines SV machines technique test set Theorem training data training examples training set Vapnik variables VC dimension Wahba zero
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