Titlebook: Braverman Readings in Machine Learning. Key Ideas from Inception to Current State; International Confer Lev Rozonoer,Boris Mirkin,Ilya Much

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Potential Functions for Signals and Symbolic Sequenceszed probabilistic featureless SVM-based approach to combining different data sources via supervised selective kernel fusion was proposed in our previous papers. In this paper we demonstrate significant qualitative advantages of the proposed approach over other methods of kernel fusion on example of

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One-Class Semi-supervised Learninginear separability in the transformed space of kernel functions. Finally, we examined the work of the proposed algorithm on the USPS dataset and analyzed the relationship of its performance and the size of the initially labeled sample.

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Prediction of Drug Efficiency by Transferring Gene Expression Data from Cell Lines to Cancer Patientoints both below and above any point that correspond to a patient. Additionally, in a manner that is a little similar to the . (kNN) method, after the selection of feature subspace, we take into account only . cell line points that are closer to a patient’s point in the selected subspace. Having var

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Misha Braverman: My Mentor and My Model the project. In the end, I asked him as a speaker, whether there was any novelty in their methods at all since I could see none in his narrative. Ilya seemed pleasantly surprised that among the audience was somebody who was able to follow his technical explanations through. He made several remarks

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