Kernel Methods for Pattern Analysis
Kernel Methods for Pattern Analysis
Convex Optimization
Learning the Kernel Matrix with Semidefinite Programming
The Journal of Machine Learning Research
Multiple kernel learning, conic duality, and the SMO algorithm
ICML '04 Proceedings of the twenty-first international conference on Machine learning
Learning the Kernel with Hyperkernels
The Journal of Machine Learning Research
Learning the Kernel Function via Regularization
The Journal of Machine Learning Research
Hierarchic Bayesian models for kernel learning
ICML '05 Proceedings of the 22nd international conference on Machine learning
Ensemble classifier for protein fold pattern recognition
Bioinformatics
Large Scale Multiple Kernel Learning
The Journal of Machine Learning Research
More efficiency in multiple kernel learning
Proceedings of the 24th international conference on Machine learning
Multiclass multiple kernel learning
Proceedings of the 24th international conference on Machine learning
Probabilistic multi-class multi-kernel learning
Bioinformatics
Multi-class Discriminant Kernel Learning via Convex Programming
The Journal of Machine Learning Research
lp-Norm Multiple Kernel Learning
The Journal of Machine Learning Research
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For many biomedical modelling tasks a number of different types of data may influence predictions made by the model. An established approach to pursuing supervised learning with multiple types of data is to encode these different types of data into separate kernels and use multiple kernel learning . In this paper we propose a simple iterative approach to multiple kernel learning (MKL), focusing on multi-class classification. This approach uses a block L 1-regularization term leading to a jointly convex formulation. It solves a standard multi-class classification problem for a single kernel, and then updates the kernel combinatorial coefficients based on mixed RKHS norms. As opposed to other MKL approaches, our iterative approach delivers a largely ignored message that MKL does not require sophisticated optimization methods while keeping competitive training times and accuracy across a variety of problems. We show that the proposed method outperforms state-of-the-art results on an important protein fold prediction dataset and gives competitive performance on a protein subcellular localization task.