Fast training of support vector machines using sequential minimal optimization
Advances in kernel methods
Text Classification from Labeled and Unlabeled Documents using EM
Machine Learning - Special issue on information retrieval
Gene functional classification from heterogeneous data
RECOMB '01 Proceedings of the fifth annual international conference on Computational biology
Transductive Inference for Text Classification using Support Vector Machines
ICML '99 Proceedings of the Sixteenth International Conference on Machine Learning
Learning the Kernel Matrix with Semi-Definite Programming
ICML '02 Proceedings of the Nineteenth International Conference on Machine Learning
Machine Learning: Discriminative and Generative (Kluwer International Series in Engineering and Computer Science)
Protein function prediction via graph kernels
Bioinformatics
Kernel methods for predicting protein--protein interactions
Bioinformatics
Discriminant kernel and regularization parameter learning via semidefinite programming
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
Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining
Representing shape with a spatial pyramid kernel
Proceedings of the 6th ACM international conference on Image and video retrieval
Localized multiple kernel learning
Proceedings of the 25th international conference on Machine learning
Multi-class Discriminant Kernel Learning via Convex Programming
The Journal of Machine Learning Research
Pattern recognition with a Bayesian kernel combination machine
Pattern Recognition Letters
Combining feature spaces for classification
Pattern Recognition
Analysis of the distance between two classes for tuning SVM hyperparameters
IEEE Transactions on Neural Networks
L2 regularization for learning kernels
UAI '09 Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence
End-to-end quality of service seen by applications: A statistical learning approach
Computer Networks: The International Journal of Computer and Telecommunications Networking
A Family of Simple Non-Parametric Kernel Learning Algorithms
The Journal of Machine Learning Research
Multiple Kernel Learning Algorithms
The Journal of Machine Learning Research
Operators for transforming kernels into quasi-local kernels that improve SVM accuracy
Journal of Intelligent Information Systems
Combining data sources nonlinearly for cell nucleus classification of renal cell carcinoma
SIMBAD'11 Proceedings of the First international conference on Similarity-based pattern recognition
Efficient semi-supervised learning on locally informative multiple graphs
Pattern Recognition
Algorithms for learning kernels based on centered alignment
The Journal of Machine Learning Research
Localized algorithms for multiple kernel learning
Pattern Recognition
Deriving kernels from generalized Dirichlet mixture models and applications
Information Processing and Management: an International Journal
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The power and popularity of kernel methods stem in part from their ability to handle diverse forms of structured inputs, including vectors, graphs and strings. Recently, several methods have been proposed for combining kernels from heterogeneous data sources. However, all of these methods produce stationary combinations; i.e., the relative weights of the various kernels do not vary among input examples. This article proposes a method for combining multiple kernels in a nonstationary fashion. The approach uses a large-margin latent-variable generative model within the maximum entropy discrimination (MED) framework. Latent parameter estimation is rendered tractable by variational bounds and an iterative optimization procedure. The classifier we use is a log-ratio of Gaussian mixtures, in which each component is implicitly mapped via a Mercer kernel function. We show that the support vector machine is a special case of this model. In this approach, discriminative parameter estimation is feasible via a fast sequential minimal optimization algorithm. Empirical results are presented on synthetic data, several benchmarks, and on a protein function annotation task.