Advances in Data Mining. Applications and Theoretical by Hongmin Cai (auth.), Petra Perner (eds.)

By Hongmin Cai (auth.), Petra Perner (eds.)

This ebook constitutes the refereed complaints of the eleventh commercial convention on information Mining, ICDM 2011, held in manhattan, united states in September 2011.

The 22 revised complete papers offered have been conscientiously reviewed and chosen from a hundred submissions. The papers are prepared in topical sections on facts mining in drugs and agriculture, info mining in advertising, facts mining for commercial techniques and in telecommunication, Multimedia facts Mining, theoretical points of knowledge mining, facts Warehousing, WebMining and knowledge Mining.

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Extra info for Advances in Data Mining. Applications and Theoretical Aspects: 11th Industrial Conference, ICDM 2011, New York, NY, USA, August 30 – September 3, 2011. Proceedings

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Such evaluation is repeated p times, and each time different part Pi′ is used as the test set. The cross-validation error rate eCVE(vλ*[n+2]) (45) is estimated in the cross validation procedure as the mean value of the error rates ei′( vλ*[n+2]) evaluated on various parts (test sets) Pi′. The cross validation procedure uses different vectors z[n+2] for the classifier designing and evaluation. In result, the bias of the error rate estimation (45) can be reduced. For the purpose of feature selection in the interval regression the CPL criterion function ΦR(w[n]) (38) has been modified in a similar manner to (42): ΨR(w[n]) = ΦR(w[n]) + λ Σ γi φi(w[n]) = ΦR(w[n]) + λ Σ γi |wi| i ∈{1,…,n} i ∈{1,…,n} (46) The minimization of the CPL criterion function ΨR(w[n]) (45) with the cost level λ allows to find the optimal vector of parameters wλ*[n]: (∃wλ*[n]) (∀w[n]) ΨR(w[n]) ≥ ΨR(wλ*[n]) (47) 22 L.

Advances in Soft Computing, vol. 2. Springer, Heidelberg (in the press, 2011) 7. : Selection of high risk patients with ranked models based on the CPL criterion functions. In: Perner, P. ) ICDM 2010. LNCS, vol. 6171, pp. 432–441. Springer, Heidelberg (2010) 8. : Eksploracja danych oparta na wypukłych i odcinkowo-liniowych funkcjach kryterialnych, Data mining based on convex and piecewise linear criterion functions, Technical University Białystok (2005) (in Polish) 9. : Design of piecewise linear classifiers from formal neurons by some basis exchange technique.

Using the same logic, if we insist that at most k out of n are outside the circle, we need to make sure that the quadratic on [dn−k , dn−k+1 ] is convex and increasing. On that interval we know that i bi = k. Hence we conclude that on this interval β(R) = 1 − kC > 0 or again: C < 1/k. Hence, ν = 1/nC > k/n is an upper bound on the fraction of points outside the descriptor (cf. [4]). – In fact, by incorporating some straightforward geometric constraints into the set-up we can further narrow down the different possible configuration.

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