A training algorithm for optimal margin classifiers
COLT '92 Proceedings of the fifth annual workshop on Computational learning theory
The nature of statistical learning theory
The nature of statistical learning theory
Machine Learning
Reducing the run-time complexity in support vector machines
Advances in kernel methods
Exploiting generative models in discriminative classifiers
Proceedings of the 1998 conference on Advances in neural information processing systems II
Proceedings of the sixth annual international conference on Computational biology
A new discriminative kernel from probabilistic models
Neural Computation
Text classification using string kernels
The Journal of Machine Learning Research
Estimation of Dependences Based on Empirical Data: Springer Series in Statistics (Springer Series in Statistics)
Minimizing the Cross Validation Error to Mix Kernel Matrices of Heterogeneous Biological Data
Neural Processing Letters
SVM-based generalized multiple-instance learning via approximate box counting
ICML '04 Proceedings of the twenty-first international conference on Machine learning
Weighted decomposition kernels
ICML '05 Proceedings of the 22nd international conference on Machine learning
2005 Special Issue: The context-tree kernel for strings
Neural Networks - Special issue on neural networks and kernel methods for structured domains
Practical solutions to the problem of diagonal dominance in kernel document clustering
ICML '06 Proceedings of the 23rd international conference on Machine learning
Structural alignment based kernels for protein structure classification
Proceedings of the 24th international conference on Machine learning
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In kernel methods, all the information about the training data is contained in the Gram matrix. If this matrix has large diagonal values, which arises for many types of kernels, then kernel methods do not perform well. We propose and test several methods for dealing with this problem by reducing the dynamic range of the matrix while preserving the positive definiteness of the Hessian of the quadratic programming problem that one has to solve when training a Support Vector Machine.