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1.
Vladimir Koltchinskii 《Annales de l'Institut Henri Poincaré (B) Probabilités et Statistiques》2003,39(6):1143-978
Let
be a probability space and let Pn be the empirical measure based on i.i.d. sample (X1,…,Xn) from P. Let
be a class of measurable real valued functions on
For
define Ff(t):=P{ft} and Fn,f(t):=Pn{ft}. Given γ(0,1], define n,γ(δ):=1/(n1−γ/2δγ). We show that if the L2(Pn)-entropy of the class
grows as −α for some α(0,2), then, for all
and all δ(0,Δn), Δn=O(n1/2), and where
and c(σ)↓1 as σ↓0 (the above inequalities hold for any fixed σ(0,1] with a high probability). Also, define Then for all
uniformly in
and with probability 1 (for
the above ratio is bounded away from 0 and from ∞). The results are motivated by recent developments in machine learning, where they are used to bound the generalization error of learning algorithms. We also prove some more general results of similar nature, show the sharpness of the conditions and discuss the applications in learning theory. 相似文献
2.
We develop a method for measuring homology classes. This involves two problems. First, we define the size of a homology class, using ideas from relative homology. Second, we define an optimal basis of a homology group to be the basis whose elements' size have the minimal sum. We provide a greedy algorithm to compute the optimal basis and measure classes in it. The algorithm runs in O(βn3log2n) time, where n is the size of the simplicial complex and β is the Betti number of the homology group. Finally, we prove the stability of our result. The algorithm can be adapted to measure any given class. 相似文献
3.
B. Z. Kacewicz 《Journal of Complexity》1988,4(4)
We deal with algorithms for solving systems z′(x) = f(x, z(x)), x ε [0, c], z(0) = η where f has r continuous bounded derivatives in [0, c) ×
s. We consider algorithms whose sole dependence on f is through the values of n linear continuous functionals at f. We show that if these functionals are defined by partial derivatives off then, roughly speaking, the error of an algorithm (for a fixed f) cannot converge to zero faster than n−r as n → +∞. This minimal error is achieved by the Taylor algorithm. If arbitrary linear continuous functionals are allowed, then the error cannot converge to zero faster than n−(r+1) as n → +∞. This minimal error is achieved by the Taylor-integral algorithm which uses integrals of f. 相似文献
4.
Let D(G) be the minimum quantifier depth of a first order sentence Φ that defines a graph G up to isomorphism. Let D0(G) be the version of D(G) where we do not allow quantifier alternations in Φ. Define q0(n) to be the minimum of D0(G) over all graphs G of order n.We prove that for all n we have
log*n−log*log*n−2≤q0(n)≤log*n+22,