Original Contribution: Stacked generalization
Neural Networks
IEEE Transactions on Pattern Analysis and Machine Intelligence
UAI '01 Proceedings of the 17th Conference in Uncertainty in Artificial Intelligence
Sparse bayesian learning and the relevance vector machine
The Journal of Machine Learning Research
Information Theory, Inference & Learning Algorithms
Information Theory, Inference & Learning Algorithms
Combining Pattern Classifiers: Methods and Algorithms
Combining Pattern Classifiers: Methods and Algorithms
Kernel Methods for Pattern Analysis
Kernel Methods for Pattern Analysis
Learning the Kernel Matrix with Semidefinite Programming
The Journal of Machine Learning Research
Learning the Kernel with Hyperkernels
The Journal of Machine Learning Research
Hierarchic Bayesian models for kernel learning
ICML '05 Proceedings of the 22nd international conference on Machine learning
Nonstationary kernel combination
ICML '06 Proceedings of the 23rd international conference on Machine learning
Gaussian Processes for Machine Learning (Adaptive Computation and Machine Learning)
Gaussian Processes for Machine Learning (Adaptive Computation and Machine Learning)
Pattern Recognition and Machine Learning (Information Science and Statistics)
Pattern Recognition and Machine Learning (Information Science and Statistics)
Ensemble classifier for protein fold pattern recognition
Bioinformatics
An empirical analysis of the probabilistic K-nearest neighbour classifier
Pattern Recognition Letters
The evidence framework applied to classification networks
Neural Computation
Probabilistic multi-class multi-kernel learning
Bioinformatics
Pattern recognition with a Bayesian kernel combination machine
Pattern Recognition Letters
ICMLA '08 Proceedings of the 2008 Seventh International Conference on Machine Learning and Applications
Kernel combination versus classifier combination
MCS'07 Proceedings of the 7th international conference on Multiple classifier systems
Feature fusion using locally linear embedding for classification
IEEE Transactions on Neural Networks
Multiclass relevance vector machines: sparsity and accuracy
IEEE Transactions on Neural Networks
Multiple Kernel Learning Algorithms
The Journal of Machine Learning Research
Online learning with multiple kernels: A review
Neural Computation
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In this paper we offer a variational Bayes approximation to the multinomial probit model for basis expansion and kernel combination. Our model is well-founded within a hierarchical Bayesian framework and is able to instructively combine available sources of information for multinomial classification. The proposed framework enables informative integration of possibly heterogeneous sources in a multitude of ways, from the simple summation of feature expansions to weighted product of kernels, and it is shown to match and in certain cases outperform the well-known ensemble learning approaches of combining individual classifiers. At the same time the approximation reduces considerably the CPU time and resources required with respect to both the ensemble learning methods and the full Markov chain Monte Carlo, Metropolis-Hastings within Gibbs solution of our model. We present our proposed framework together with extensive experimental studies on synthetic and benchmark datasets and also for the first time report a comparison between summation and product of individual kernels as possible different methods for constructing the composite kernel matrix.