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Efficient and Accurate Lp-Norm Multiple Kernel Learning
Previous approaches to multiple kernel learning (MKL) promote sparse kernel combinations and hence support interpretability.
Transductive Inference and Semi-Supervised Learning
This chapter contains sections titled: Problem Settings, Problem of Generalization in Inductive and Transductive Inference, Structure of the VC Bounds and Transductive Inference, The Symmetrization Lemma and Transductive Inference, Bounds for Transductive Inference, The Structural Risk Minimization Principle for Induction and Transduction, Combinatorics in Transductive Inference, Measures of the Size of Equivalence Classes, Algorithms for Inductive and Transductive SVMs, Semi-Supervised Learning, Conclusion: Transductive Inference and the New Problems of Inference, Beyond Transduction: Selective Inference
