An introduction to support Vector Machines: and other kernel-based learning methods
An introduction to support Vector Machines: and other kernel-based learning methods
Gene functional classification from heterogeneous data
RECOMB '01 Proceedings of the fifth annual international conference on Computational biology
Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond
Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond
Choosing Multiple Parameters for Support Vector Machines
Machine Learning
MARK: a boosting algorithm for heterogeneous kernel models
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
Text classification using string kernels
The Journal of Machine Learning Research
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
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
Adaptive mixtures of local experts
Neural Computation
Localized multiple kernel learning
Proceedings of the 25th international conference on Machine learning
Building sparse multiple-kernel SVM classifiers
IEEE Transactions on Neural Networks
Supervised learning of local projection kernels
Neurocomputing
Per-sample multiple kernel approach for visual concept learning
Journal on Image and Video Processing - Special issue on selected papers from multimedia modeling conference 2009
Multiclass relevance vector machines: sparsity and accuracy
IEEE Transactions on Neural Networks
Localized Multiple Kernel Regression
ICPR '10 Proceedings of the 2010 20th International Conference on Pattern Recognition
Multiple Kernel Learning Algorithms
The Journal of Machine Learning Research
Local linear perceptrons for classification
IEEE Transactions on Neural Networks
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Instead of selecting a single kernel, multiple kernel learning (MKL) uses a weighted sum of kernels where the weight of each kernel is optimized during training. Such methods assign the same weight to a kernel over the whole input space, and we discuss localized multiple kernel learning (LMKL) that is composed of a kernel-based learning algorithm and a parametric gating model to assign local weights to kernel functions. These two components are trained in a coupled manner using a two-step alternating optimization algorithm. Empirical results on benchmark classification and regression data sets validate the applicability of our approach. We see that LMKL achieves higher accuracy compared with canonical MKL on classification problems with different feature representations. LMKL can also identify the relevant parts of images using the gating model as a saliency detector in image recognition problems. In regression tasks, LMKL improves the performance significantly or reduces the model complexity by storing significantly fewer support vectors.