The nature of statistical learning theory
The nature of statistical learning theory
Machine Learning
Fast training of support vector machines using sequential minimal optimization
Advances in kernel methods
An introduction to support Vector Machines: and other kernel-based learning methods
An introduction to support Vector Machines: and other kernel-based learning methods
Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond
Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond
Choosing Multiple Parameters for Support Vector Machines
Machine Learning
On the Performance Assessment and Comparison of Stochastic Multiobjective Optimizers
PPSN IV Proceedings of the 4th International Conference on Parallel Problem Solving from Nature
Proceedings of the 5th International Conference on Intelligent Systems for Molecular Biology
Radius margin bounds for support vector machines with the RBF kernel
Neural Computation
Learning the Kernel Matrix with Semidefinite Programming
The Journal of Machine Learning Research
Markov Encoding for Detecting Signals in Genomic Sequences
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
Learning the Kernel with Hyperkernels
The Journal of Machine Learning Research
Learning by Kernel Polarization
Neural Computation
Gradient-Based Adaptation of General Gaussian Kernels
Neural Computation
Working Set Selection Using Second Order Information for Training Support Vector Machines
The Journal of Machine Learning Research
Maximum-Gain Working Set Selection for SVMs
The Journal of Machine Learning Research
Evolutionary tuning of multiple SVM parameters
Neurocomputing
Evolutionary optimization of sequence kernels for detection of bacterial gene starts
ICANN'06 Proceedings of the 16th international conference on Artificial Neural Networks - Volume Part II
Efficient tuning of SVM hyperparameters using radius/margin bound and iterative algorithms
IEEE Transactions on Neural Networks
Optimizing the kernel in the empirical feature space
IEEE Transactions on Neural Networks
The Journal of Machine Learning Research
Learning by local kernel polarization
Neurocomputing
Feature selection for SVM via optimization of kernel polarization with Gaussian ARD kernels
Expert Systems with Applications: An International Journal
Weighting informativeness of bag-of-visual-words by kernel optimization for video concept detection
Proceedings of the international workshop on Very-large-scale multimedia corpus, mining and retrieval
Multiple Kernel Learning Algorithms
The Journal of Machine Learning Research
Neurocomputing
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Biological data mining using kernel methods can be improved by a task-specific choice of the kernel function. Oligo kernels for genomic sequence analysis have proven to have a high discriminative power and to provide interpretable results. Oligo kernels that consider subsequences of different lengths can be combined and parameterized to increase their flexibility. For adapting these parameters efficiently, gradient-based optimization of the kernel-target alignment is proposed. The power of this new, general model selection procedure and the benefits of fitting kernels to problem classes are demonstrated by adapting oligo kernels for bacterial gene start detection.