An introduction to support Vector Machines: and other kernel-based learning methods
An introduction to support Vector Machines: and other kernel-based learning methods
Everything old is new again: a fresh look at historical approaches in machine learning
Everything old is new again: a fresh look at historical approaches in machine learning
Rademacher and gaussian complexities: risk bounds and structural results
The Journal of Machine Learning Research
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
Learning the Kernel Function via Regularization
The Journal of Machine Learning Research
Proteome Profiling without Selection Bias
CBMS '06 Proceedings of the 19th IEEE Symposium on Computer-Based Medical Systems
Frames, Reproducing Kernels, Regularization and Learning
The Journal of Machine Learning Research
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In this paper we propose a strategy for constructing data-driven kernels, automatically determined by the training examples. Basically, their associated Reproducing Kernel Hilbert Spaces arise from finite sets of linearly independent functions, that can be interpreted as weak classifiers or regressors, learned from training material. When working in the Tikhonov regularization framework, the unique free parameter to be optimized is the regularizer, representing a trade-off between empirical error and smoothness of the solution. A generalization error bound based on Rademacher complexity is provided, yielding the potential for controlling overfitting.