منابع مشابه
Fairness in Learning: Classic and Contextual Bandits
We introduce the study of fairness in multi-armed bandit problems. Our fairness definition demands that, given a pool of applicants, a worse applicant is never favored over a better one, despite a learning algorithm’s uncertainty over the true payoffs. In the classic stochastic bandits problem we provide a provably fair algorithm based on “chained” confidence intervals, and prove a cumulative r...
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The ability to acquire non-linearly separable (NLS) classifications is well documented in the study of human category learning. In particular, one experiment (Medin & Schwanenflugel, 1981; E4) is viewed as the canonical demonstration that, when withinand betweencategory similarities are evenly matched, NLS classifications are not more difficult to acquire than linearly separable ones. The resul...
متن کاملClassic Migraine or Not Classic Migraine
OBJECTIVE To identify the main characteristics of classic migraine, with specific regard to diagnostic criteria for manual therapy practitioners, including chiropractors and osteopaths. METHOD Ten case studies on migraine were reviewed for the symptoms and clinical features. RESULTS The majority of cases reviewed as classic migraines were in reality not correct diagnoses in accordance with ...
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In the fall of 2007, 2 years shy of her 100th birthday, Nobel laureate and National Academy of Sciences member Rita Levi-Montalcini yearned to start a new research project. Her idea stemmed from studies she had carried out more than half a century earlier, through which she unveiled and characterized the function of NGF. Time had taken its toll on her sight and hearing, but her keen observation...
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ژورنال
عنوان ژورنال: Machine Learning
سال: 1997
ISSN: 0885-6125,1573-0565
DOI: 10.1007/bf00114009