The nature of statistical learning theory
The nature of statistical learning theory
An equivalence between sparse approximation and support vector machines
Neural Computation
Advances in kernel methods: support vector learning
Advances in kernel methods: support vector learning
Atomic Decomposition by Basis Pursuit
SIAM Journal on Scientific Computing
AI Game Programming Wisdom
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Relation between a family of generalized Support Vector Machine (SVM) problems and the novel ε-sparse representation is provided. In defining ε-sparse representations, we use a natural generalization of the classical ε-insensitive cost function for vectors. The insensitive parameter of the SVM problem is transformed into component-wise insensitivity and thus overall sparsification is replaced by component-wise sparsification. The connection between these two problems is built through the generalized Moore-Penrose inverse of the Gram matrix associated to the kernel.