Varying irrelevant phonetic features hinders learning of the feature being trained
نویسندگان
چکیده
منابع مشابه
Learning with Many Irrelevant Features
In many domains, an appropriate inductive bias is the MIN-FEATURES bias, which prefers consistent hypotheses deenable over as few features as possible. This paper deenes and studies this bias. First, it is shown that any learning algorithm implementing the MIN-FEATURES bias requires (1 ln 1 + 1 2 p + p lnn]) training examples to guarantee PAC-learning a concept having p relevant features out of...
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ژورنال
عنوان ژورنال: The Journal of the Acoustical Society of America
سال: 2016
ISSN: 0001-4966
DOI: 10.1121/1.4939736