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یادگیری ماشین 2013
جزئیات رویداد
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  • sequential approaches for learning datum-wise sparse representations

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    • تاریخ ارائه: 1392/07/24
    • تاریخ انتشار در تی پی بین: 1392/07/24
    • تعداد بازدید: 1063
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     in supervised classification, data representation is usually considered at the dataset level: one looks for the “best” representation of data assuming it to be the same for all the data in the data space. we propose a different approach where the representations used for classification are tailored to each datum in the data space. one immediate goal is to obtain sparse datum-wise representations: our approach learns to build a representation specific to each datum that contains only a small subset of the features, thus allowing classification to be fast and efficient. this representation is obtained by way of a sequential decision process that sequentially chooses which features to acquire before classifying a particular point; this process is learned through algorithms based on reinforcement learning.

    the proposed method performs well on an ensemble of medium-sized sparse classification problems. it offers an alternative to global sparsity approaches, and is a natural framework for sequential classification problems. the method extends easily to a whole family of sparsity-related problem which would otherwise require developing specific solutions. this is the case in particular for cost-sensitive and limited-budget classification, where feature acquisition is costly and is often performed sequentially. finally, our approach can handle non-differentiable loss functions or combinatorial optimization encountered in more complex feature selection problems.

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