By COLT
The court cases of COLT ninety one specialise in quantitative theories of computer studying. themes contain analyses for quite a few versions of significant parameters of laptop studying, reminiscent of computational rate, accuracy of generalization, and the variety of interactions wanted. No index. Annotation copyright Bo
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Extra info for COLT. Proceedings of the Fourth Annual Workshop on Computational Learning Theory
Example text
Assume that g simultaneously learns (V,C). Given e and δ, pick JV large enough that X(N) : sup P(g[X,XC]Ac) cec > e/2 :={g[X,b}-be{0,l} }. e. l. e. X(N) N VX N :cuc2GC} and T h e o r e m 5 . 1 . For arbitrary set and then estimate its true probability by a relative frequency estimate. Consider the set of values that g can take on X, <δ/2 for any P G V. Given this many samples labeled by an unknown set, we can use g to approximate that y(M) : max \Py(d) - P(d)\ > e/2 <δ/2. An / ( N + M ) that simultaneously estimates (V,C) with accuracy e and confidence δ results when we compose these two approximations: f(N+M)[(x,y),(Xc,yc)] = Py(g[x,xc}).
1 that the learnability theorems proved in Section 3 are fairly invariant with respect to the particular shape and/or size of the neighborhood being utilized for smoothing. e. fixed, but arbitrary, positive integer (dimension size) accuracy/confidence parameters upper case alphabets: sets in euclidean space (real) parameters Greek letters: boldfaced alphabets: collections of sets in euclidean space or distribution collections adopted terminologies b o l d f a c e d words: r m m points in euclidean space B x = {y G R |doo(*,y) < r} (Vx G R , r > 0 ) .
Y = { - y | y G Y } (VY G m P ( R ) ) , then the set- dilation and erosion operations can be seen to have the following properties: m VX,Y,ZGP(R ), χ e y = ( J x = {ζ\(Ϋ) y χ ζ η χ φ 0} e γ = γ e X; ( x e ζ ) υ (υ θ ζ ) = χ θ Υ = r ; (χ υ γ ) m p | X _ y= {^GR |Y, ς χ } Χ θ B x = (x θ ζ; e c ; m φ £ £ ) Vx G R , r > 0. In particular, we note that uj=l({ut} θ BQ) m { t u , . . , Μ θ 5g (Vti, G R , < = 1,2,... e. x C Y implies x 0 ζ m Υ φ ζ (VX,Y,Z G P ( R ) ) . = 0). 2 L E A R N A B I L I T Y TOOLS e x a m p l e points < uuxc(ui) We state in this section some definitions and theorems pertaining to polynomial learnability.