A. Blum. Learning in an infinite attribute space. Proceedings of the 22nd A.C.M. Symposium on the Theory of Computing, 1990, pp. 64-72. Home/Search Document Not in Database Summary Related Articles Check |
This paper is cited in the following contexts:
Learning in the Presence of Malicious Errors - Kearns (1993) (81 citations) (Correct)
....will tolerate a larger rate of error when the number s of relevant attributes is considerably smaller than the total number of variables n. Other improvements in the performance of learning algorithms in the presence of many irrelevant attributes are investigated by Littlestone [16] and Blum [3]. We note that by applying Theorem 2 we can show that even for M 1 n , the class of monomials of length 1, the positive only and negative only malicious error rates are bounded by ffl= n Gamma 1) This is again an absolute bound, holding regardless of the computational complexity of the ....
A. Blum. Learning in an infinite attribute space. Proceedings of the 22nd A.C.M. Symposium on the Theory of Computing, 1990, pp. 64-72.
Learning in the Presence of Malicious Errors - Michael Kearns Att (1993) (81 citations) (Correct)
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A. Blum. Learning in an infinite attribute space. Proceedings of the 22nd A.C.M. Symposium on the Theory of Computing, 1990, pp. 64-72.
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