By William W. Cohen (auth.), Hiroki Arimura, Sanjay Jain, Arun Sharma (eds.)
This e-book constitutes the refereed court cases of the eleventh overseas convention on Algorithmic studying concept, ALT 2000, held in Sydney, Australia in December 2000.
The 22 revised complete papers provided including 3 invited papers have been conscientiously reviewed and chosen from 39 submissions. The papers are geared up in topical sections on statistical studying, inductive common sense programming, inductive inference, complexity, neural networks and different paradigms, help vector machines.
Read or Download Algorithmic Learning Theory: 11th International Conference, ALT 2000 Sydney, Australia, December 11–13, 2000 Proceedings PDF
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Additional info for Algorithmic Learning Theory: 11th International Conference, ALT 2000 Sydney, Australia, December 11–13, 2000 Proceedings
Though it is simpliﬁed, the adaptive sampling part is essentially the same as the original 34 Osamu Watanabe Adaptive Sampling begin m ← 0; n ← 0; while m < A do get x uniformly at random from D; m ← m + B(x); n ← n + 1; output m/n as an approximation of pB ; end. Fig. 2. Adaptive Sampling one. As we can see, the structure of the algorithm is simple. It runs until it sees more than A examples x with B(x) = 1. To complete the description of the algorithm, we have to specify the way to determine A.
Now these two bounds are stated as follows. ) Theorem 1. (The Hoeﬀding Bound) For any , 0 < < 1, we have the following relations. Pr X >p+ n ≤ exp(−2n 2 ), Pr X
(1 + ε)p ≤ exp − n 3 , Pr X pnε2 < (1 − ε)p ≤ exp − n 2 . By using these bounds, we calculate “safe” sample size, the number n of examples, so that Batch Sampling satisﬁes our approximation goals. , bounding the absolute estimation error.
Here we explain the Hoeﬀding bound  and the Chernoﬀ bound  that have been used in computer science. , smaller) sample size. But the Cen- 30 Osamu Watanabe tral Limit Theorem holds only asymptotically, and furthermore, the diﬀerence is within a constant factor. ) For explaining these bounds, let us prepare some notations. , Xn be independent trials, which are called Bernoulli trials, such that, for 1 ≤ i ≤ n, we have Pr[Xi = 1] = p and Pr[Xi = 0] = 1 − p for some p, 0 < p < 1. Let X be a random variable deﬁned by X = ni=1 Xi .
Algorithmic Learning Theory: 11th International Conference, ALT 2000 Sydney, Australia, December 11–13, 2000 Proceedings by William W. Cohen (auth.), Hiroki Arimura, Sanjay Jain, Arun Sharma (eds.)