Introduction to statistical pattern recognition (2nd ed.)
Introduction to statistical pattern recognition (2nd ed.)
The nature of statistical learning theory
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Least Squares Support Vector Machine Classifiers
Neural Processing Letters
An introduction to support Vector Machines: and other kernel-based learning methods
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Proximal support vector machine classifiers
Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining
Machine Learning
Learning from Data: Concepts, Theory, and Methods
Learning from Data: Concepts, Theory, and Methods
Some Notes on Alternating Optimization
AFSS '02 Proceedings of the 2002 AFSS International Conference on Fuzzy Systems. Calcutta: Advances in Soft Computing
MARK: a boosting algorithm for heterogeneous kernel models
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
Convergence of alternating optimization
Neural, Parallel & Scientific Computations
Learning the Kernel Matrix with Semidefinite Programming
The Journal of Machine Learning Research
Hierarchic Bayesian models for kernel learning
ICML '05 Proceedings of the 22nd international conference on Machine learning
Optimal kernel selection in Kernel Fisher discriminant analysis
ICML '06 Proceedings of the 23rd international conference on Machine learning
Discriminant kernel and regularization parameter learning via semidefinite programming
Proceedings of the 24th international conference on Machine learning
Multiclass multiple kernel learning
Proceedings of the 24th international conference on Machine learning
Nonlinear adaptive distance metric learning for clustering
Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining
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Multi-class Discriminant Kernel Learning via Convex Programming
The Journal of Machine Learning Research
Learning sparse kernels from 3D surfaces for heart wall motion abnormality detection
IAAI'08 Proceedings of the 20th national conference on Innovative applications of artificial intelligence - Volume 3
Feature selection and kernel design via linear programming
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
Multi-class classifiers and their underlying shared structure
IJCAI'09 Proceedings of the 21st international jont conference on Artifical intelligence
Kernel combination versus classifier combination
MCS'07 Proceedings of the 7th international conference on Multiple classifier systems
Multiple Kernel Learning Algorithms
The Journal of Machine Learning Research
A case for learning simpler rule sets with multiobjective evolutionary algorithms
RuleML'2011 Proceedings of the 5th international conference on Rule-based reasoning, programming, and applications
Generalized augmentation of multiple kernels
MCS'11 Proceedings of the 10th international conference on Multiple classifier systems
Generalized foley-sammon transform with kernels
ISNN'05 Proceedings of the Second international conference on Advances in Neural Networks - Volume Part I
Discriminant analysis in pairwise kernel learning for SVM classification
International Journal of Bioinformatics Research and Applications
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We propose a fast iterative classification algorithm for Kernel Fisher Discriminant (KFD) using heterogeneous kernel models. In contrast with the standard KFD that requires the user to predefine a kernel function, we incorporate the task of choosing an appropriate kernel into the optimization problem to be solved. The choice of kernel is defined as a linear combination of kernels belonging to a potentially large family of different positive semidefinite kernels. The complexity of our algorithm does not increase significantly with respect to the number of kernels on the kernel family. Experiments on several benchmark datasets demonstrate that generalization performance of the proposed algorithm is not significantly different from that achieved by the standard KFD in which the kernel parameters have been tuned using cross validation. We also present results on a real-life colon cancer dataset that demonstrate the efficiency of the proposed method.