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**837 items matching your criteria.**

- 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 ...