The nature of statistical learning theory
The nature of statistical learning theory
A Tutorial on Support Vector Machines for Pattern Recognition
Data Mining and Knowledge Discovery
MARK: a boosting algorithm for heterogeneous kernel models
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
Combining Pattern Classifiers: Methods and Algorithms
Combining Pattern Classifiers: Methods and Algorithms
Learning the Kernel Matrix with Semidefinite Programming
The Journal of Machine Learning Research
A fast iterative algorithm for fisher discriminant using heterogeneous kernels
ICML '04 Proceedings of the twenty-first international conference on Machine learning
Learning the Kernel with Hyperkernels
The Journal of Machine Learning Research
LIBSVM: A library for support vector machines
ACM Transactions on Intelligent Systems and Technology (TIST)
Localized multiple kernel learning
Proceedings of the 25th international conference on Machine learning
Pattern recognition with a Bayesian kernel combination machine
Pattern Recognition Letters
Combining feature spaces for classification
Pattern Recognition
Optimal Double-Kernel Combination for Classification
MLDM '09 Proceedings of the 6th International Conference on Machine Learning and Data Mining in Pattern Recognition
Multiple Kernel Learning Algorithms
The Journal of Machine Learning Research
Generalized augmentation of multiple kernels
MCS'11 Proceedings of the 10th international conference on Multiple classifier systems
A multiple Kernel learning algorithm for cell nucleus classification of renal cell carcinoma
ICIAP'11 Proceedings of the 16th international conference on Image analysis and processing: Part I
Generalised bottom-up pruning: A model level combination of decision trees
Expert Systems with Applications: An International Journal
Online Multiple Kernel Classification
Machine Learning
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Combining classifiers is to join the strengths of different classifiers to improve the classification performance. Using rules to combine the outputs of different classifiers is the basic structure of classifier combination. Fusing models from different kernel machine classifiers is another strategy for combining models called kernel combination. Although classifier combination and kernel combination are very different strategies for combining classifier, they aim to reach the same goal by very similar fundamental concepts. We propose here a compositional method for kernel combination. The new composed kernel matrix is an extension and union of the original kernel matrices. Generally, kernel combination approaches relied heavily on the training data and had to learn some weights to indicate the importance of each kernel. Our compositional method avoids learning any weight and the importance of the kernel functions are directly derived in the process of learning kernel machines. The performance of the proposed kernel combination procedure is illustrated by some experiments in comparison with classifier combining based on the same kernels.