The perceptron algorithm versus winnow: linear versus logarithmic mistake bounds when few input variables are relevant
نویسندگان
چکیده
منابع مشابه
The Perceptron Algorithm Versus Winnow: Linear Versus Logarithmic Mistake Bounds when Few Input Variables are Relevant (Technical Note)
We give an adversary strategy that forces the Perceptron algorithm to make a( kN) mistakes in learning monotone disjunctions over N variables with at most k literals. In contrast, Littlestone’s algorithm Winnow makes at most 0( k log N) mistakes for the same problem. Both algorithms use thresholded linear functions as their hypotheses. However, Winnow does multiplicative updates to its weight v...
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ژورنال
عنوان ژورنال: Artificial Intelligence
سال: 1997
ISSN: 0004-3702
DOI: 10.1016/s0004-3702(97)00039-8