Efficiently learns halfspaces with malicious noise, near-optimal label complexity.
arXiv research
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We study the problem of efficient PAC active learning of homogeneous linear classifiers (halfspaces) in , where the goal is to learn a halfspace with low error using as few label queries as possible. Under the extra assumption that there is a -sparse halfspace that performs well on the data ()…
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In this paper we analyze a budgeted learning setting, in which the learner can only choose and observe a small subset of the attributes of each training example. We develop efficient algorithms for ridge and lasso linear regression, which utilize the geometry of the data by a novel data-dependent sampling scheme. When …
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New algorithm learns halfspaces with adversarial noise efficiently.