It's a Bernoulli noise. define f (x) = 1/ (1 + e(-r(x)) ) and the objective function at x is 1 with probability f(x). So the expected value at x is f(x), but the values you get are noisy.
Best regards, Olivier 2013/3/6 Chin-Chang Yang <[email protected]> > > > 2013/3/6 Olivier Teytaud <[email protected]> > >> >>> The CLOP is for noisy black-box parameter tuning. However, your test >>> functions (LOG, FLAT, POWER, ANGLE, and STEP) are noise-free functions as >>> shown in Table 1. It is very difficult to prove that CLOP can work very >>> well on noisy functions. >>> >> >> Waow :-) that would be a very strange noisy optimization paper if it was >> about testing on noise-free functions. >> The functions are certainly not noise-free; what you read (and which is >> noise-free...) is their _expected_ values. >> >> > > Thanks for replying me that what I read is their expected values. > > Since the functions are not noise-free, they should be defined in terms > of some noise. I really need the definition of the noise for comparison > between CLOP and other optimizers. > > I have downloaded the source codes, but I cannot find the codes related to > the noise currently. > > Chin-Chang Yang, 2013/03/06 > > _______________________________________________ > Computer-go mailing list > [email protected] > http://dvandva.org/cgi-bin/mailman/listinfo/computer-go > -- ========================================================= Olivier Teytaud, [email protected], TAO, LRI, UMR 8623(CNRS - Univ. Paris-Sud), bat 490 Univ. Paris-Sud F-91405 Orsay Cedex France http://www.slideshare.net/teytaud
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