An Introduction to Variational Methods for Graphical Models
Machine Learning
Structural Modelling with Sparse Kernels
Machine Learning
Variational Relevance Vector Machines
UAI '00 Proceedings of the 16th Conference on Uncertainty in Artificial Intelligence
Variational methods for inference and estimation in graphical models
Variational methods for inference and estimation in graphical models
A family of algorithms for approximate bayesian inference
A family of algorithms for approximate bayesian inference
Sparse bayesian learning and the relevance vector machine
The Journal of Machine Learning Research
Information Theory, Inference & Learning Algorithms
Information Theory, Inference & Learning Algorithms
Kernel Methods for Pattern Analysis
Kernel Methods for Pattern Analysis
Learning the Kernel Matrix with Semidefinite Programming
The Journal of Machine Learning Research
ICML '04 Proceedings of the twenty-first international conference on Machine learning
A fast iterative algorithm for fisher discriminant using heterogeneous kernels
ICML '04 Proceedings of the twenty-first international conference on Machine learning
Multiple kernel learning, conic duality, and the SMO algorithm
ICML '04 Proceedings of the twenty-first international conference on Machine learning
Incremental Relevance Vector Machine with Kernel Learning
SETN '08 Proceedings of the 5th Hellenic conference on Artificial Intelligence: Theories, Models and Applications
Kernel-Based Inductive Transfer
ECML PKDD '08 Proceedings of the European conference on Machine Learning and Knowledge Discovery in Databases - Part II
Pattern recognition with a Bayesian kernel combination machine
Pattern Recognition Letters
Combining feature spaces for classification
Pattern Recognition
Class Prediction from Disparate Biological Data Sources Using an Iterative Multi-Kernel Algorithm
PRIB '09 Proceedings of the 4th IAPR International Conference on Pattern Recognition in Bioinformatics
Sparse Bayesian modeling with adaptive kernel learning
IEEE Transactions on Neural Networks
Using Kernel Basis with Relevance Vector Machine for Feature Selection
ICANN '09 Proceedings of the 19th International Conference on Artificial Neural Networks: Part II
Feature fusion using locally linear embedding for classification
IEEE Transactions on Neural Networks
Rademacher chaos complexities for learning the kernel problem
Neural Computation
Constructing nonlinear discriminants from multiple data views
ECML PKDD'10 Proceedings of the 2010 European conference on Machine learning and knowledge discovery in databases: Part I
Multiclass relevance vector machines: sparsity and accuracy
IEEE Transactions on Neural Networks
Multiple Kernel Learning Algorithms
The Journal of Machine Learning Research
Large scale multikernel RVM for object detection
SETN'06 Proceedings of the 4th Helenic conference on Advances in Artificial Intelligence
Localized algorithms for multiple kernel learning
Pattern Recognition
Online learning with multiple kernels: A review
Neural Computation
| Hi-index | 0.00 |
The integration of diverse forms of informative data by learning an optimal combination of base kernels in classification or regression problems can provide enhanced performance when compared to that obtained from any single data source. We present a Bayesian hierarchical model which enables kernel learning and present effective variational Bayes estimators for regression and classification. Illustrative experiments demonstrate the utility of the proposed method. Matlab code replicating results reported is available at http://www.dcs.gla.ac.uk/~srogers/kernel_comb.html.