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Extracting support data for a given task [1%] by admin, 2007-01-31 11:07
This paper reports that different SV classifiers constructed by using different kernels (polynomial , RBF, neural net) extract the same Support ...
The Nature of Statistical Learning Theory [1%] by admin, 2007-01-31 11:07
A very good high-level introduction in Statistical Learning Theory in the VC formulation, plus a comprehensive overview of the SV algorithm. First ...
Prediction of Generalization Ability in Learning Machines [1%] by admin, 2007-01-31 11:07
In this thesis the soft margin classifier is introduced and discussed in great detail. Moreover the concept of effective VC-dimensions is introduced.
Discovering informative patterns and data cleaning. [1%] by admin, 2007-01-31 11:07
SVM's are used as a method for discovering informative patterns such that large databases can be reduced to only few representative data entries. This ...
K\"unstliches Lernen [1%] by admin, 2007-01-31 11:07
High-level summary of some aspects of learning theory and SV machines (in German).
Simplified support vector decision rules [1%] by admin, 2007-01-31 11:07
Proposes the "Reduced Set Method" , which speeds up SV machines by representing the SV solution in terms of a smaller number of vectors.
Comparison of view–based object recognition algorithms using realistic 3D models [1%] by admin, 2007-01-31 11:07
Application of SV classifiers to chair recognition, in a performance comparison (SV outperforms Neural Net and Oriented Filter approach).
Incorporating Invariances in Support Vector Learning Machines [1%] by admin, 2007-01-31 11:07
The first successful attempt to improve SV accuracy by incorporating domain knowledge, using the "Virtual SV" method.
Nonlinear component analysis as a kernel Eigenvalue problem [1%] by admin, 2007-01-31 11:07
Applies the kernel method to unsupervised algorithms as for instance Principal Component Analysis. This gives a principled and efficient approach to ...
Comparing support vector machines with Gaussian kernels to radial basis function classifiers [1%] by admin, 2007-01-31 11:07
Regression Estimation with Support Vector Learning Machines [1%] by admin, 2007-01-31 11:07
Explains Support Vector Regression with variable cost functions and a large selection of possible kernels (including a description how to implement SV ...
Improving the accuracy and speed of support vector learning machines [1%] by admin, 2007-01-31 11:07
A combination of the Reduced Set and Virtual SV methods leads to fast high-accuracy classifier.
Support vector method for function approximation, regression estimation, and signal processing [1%] by admin, 2007-01-31 11:07
First implementation of SV regression, and first discussion of spline kernels.
Support vector regression machines [1%] by admin, 2007-01-31 11:07
Reports empirical results on SV regression.
On a Kernel–based Method for Pattern Recognition, Regression, Approximation and Operator Inversion [1%] by admin, 2007-01-31 11:07
Generalizes the SV approach to a wider range of cost functions, and establishes a link between regularization operators and SV kernels.
Support Vector Machines: Training and Applications [1%] by admin, 2007-01-31 11:07
Pedestrian Detection Using Wavelet Templates [1%] by admin, 2007-01-31 11:07
Training Support Vector Machines: An Application to Face Detection [1%] by admin, 2007-01-31 11:07
Formally proves the first decomposition training algorithm for SVMs. Application of SV learning to a large-scale Computer Vision problem (Face ...
An Improved Training Algorithm for Support Vector Machines [1%] by admin, 2007-01-31 11:07
Formulates a decomposition algorithm for training SVMs with large numbers of SVs.
Nonlinear Prediction of Chaotic Time Series using a Support Vector Machine [1%] by admin, 2007-01-31 11:07
Uses SV regression on chaotic time-series (Mackey-Glass, Ikewda Map and Lorenz) and compares with other techniques reported (Casdalgi).
From Regularization Operators to Support Vector Kernels [1%] by admin, 2007-01-31 11:07
A correspondence between regularization operators and Support Vector kernels is derived by using Green's Functions. It is shown that a large number of ...
Prior Knowledge in Support Vector Kernels [1%] by admin, 2007-01-31 11:07
Methods for incorporating prior knowledge in Support Vector machines are explored. It is shown that both invariances under group transformations and ...
Support Vector Learning [1%] by admin, 2007-01-31 11:07
Mainly about incorporating prior knowledge in SV machines, and about Kernel PCA.
Support Vector Machines, Reproducing Kernel Hilbert Spaces and the Randomized GACV [1%] by admin, 2007-01-31 11:07
Review of some old but relevant results on constrained optimization in Reproducing Kernel Hilbert Spaces and SVM's. Application of spline ANOVA spaces ...
A Support Vector Machine Approach to Decision Trees [1%] by admin, 2007-01-31 11:07
General cost functions for Support Vector Regression [1%] by admin, 2007-01-31 11:07
The concept of Support Vector Regression is extended to a more general class of convex cost functions. Moreover it is shown how the resulting convex ...
The Connection between Regularization Operators and Support Vector Kernels [1%] by admin, 2007-01-31 11:07
A correspondence is derived between regularization operators used in Regularization Networks and Support Vector Kernels. It is shown that Green's ...
Support Vector Machines for nonparametric binary hypothesis testing [1%] by admin, 2007-01-31 11:07
The method of support vectors for learning binary functions is generalized to the case in which the cost of the two types of error is different.
Learning and feature extraction with support vector methods [1%] by admin, 2007-01-31 11:07
This paper explains the notion of feature spaces (reproducing kernel Hilbert spaces), and reviews the SV algorithm and Kernel PCA. You can also ...
Reducing run-time complexity in SVMs [1%] by admin, 2007-01-31 11:07
The authors focus on SVM for classification and investigate the problem of reducing its run-time complexity. Two results are presented: a) the use of ...

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