Read e-book online Advances in Computing Science — ASIAN 2000: 6th Asian PDF

By Jean Vuillemin (auth.), He Jifeng, Masahiko Sato (eds.)

ISBN-10: 3540414282

ISBN-13: 9783540414285

The Asian Computing technological know-how convention (ASIAN) sequence used to be initiated in 1995 to supply a discussion board for researchers in desktop technological know-how in Asia to fulfill and to advertise interplay with researchers from different areas. the former ?ve meetings have been held, respectively, in Bangkok, Singapore, Kathmandu, Manila, and Phuket. The complaints have been released within the Lecture Notes in computing device technology sequence of Springer-Verlag. This year’s convention (ASIAN2000) attracted sixty one submissions from which 18 papers have been chosen via an digital application committee (PC) assembly. the topics for this year’s convention are: – Logics in computing device technological know-how – facts Mining – Networks and function the main be aware speaker for ASIAN2000 is Jean Vuillemin (ENS, France) and the invited audio system are Ramamohanarao Kotagiri (U. Melbourne, Australia) and Alain Jean-Marie (LIRMM, France). We thank them for accepting our - vitation. This year’s convention is subsidized by means of the Asian Institute of know-how (Thailand), INRIA (France), the nationwide collage of Singapore (Singapore), and UNU/IIST (Macau SAR, China). We thank these kinds of associations for his or her persisted help of the ASIAN sequence. This year’s convention may be held in Penang, Malaysia. we're a lot obliged to Universiti Sains Malaysia and Penang country govt for delivering the convention venue and to Dr. Abdullah Zawawi Haji Talib for making the neighborhood preparations. We additionally desire to thank the computer individuals and the massive variety of referees for thesubstantialworkputinbytheminassessingthesubmittedpapers.

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Extra info for Advances in Computing Science — ASIAN 2000: 6th Asian Computing Science Conference Penang, Malaysia, November 25–27, 2000 Proceedings

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DeEPs wins on 26 data sets; k-NN wins on 11 data sets. We conclude that the accuracy of DeEPs is generally better than that of k-NN. 5 times slower than k-NN does. The main reason is that DeEPs needs to conduct border operations. 2. 0. 0 wins on 14 data sets. 0. 0. However, DeEPs takes an instancebased learning strategy. 3. 0. 0 wins on 14 data sets. An important conclusion we can reach here is that DeEPs is an accurate instance-based classifier. 5 and k-nearest neighbor. However, the speed of DeEPs requires great improvement to compete well with other classifiers.

According to Proposition 1, there exists a unique border for any large space or any small space. The left bound of any large space is {∅}, and the right bound is the set of the maximal large itemsets. The Max-Miner algorithm [3] can be used to discover the maximal large itemsets with respect to a support threshold in a data set. In the following subsection, we describe horizontal spaces, a special type of large space. Horizontal spaces are useful for us in rewriting and computing JEP spaces. 3 Using Horizontal Spaces to Rewrite and Compute JEP Spaces Given a data set D, all non-zero support itemsets X, namely, suppD (X) = 0, form a convex space.

Emerging Patterns and Classification 29 1. DeEPs versus k-NN. – Both DeEPs and k-NN perform equally accurately on soybean-small (100%) and on iris (96%). – DeEPs wins on 26 data sets; k-NN wins on 11 data sets. We conclude that the accuracy of DeEPs is generally better than that of k-NN. 5 times slower than k-NN does. The main reason is that DeEPs needs to conduct border operations. 2. 0. 0 wins on 14 data sets. 0. 0. However, DeEPs takes an instancebased learning strategy. 3. 0. 0 wins on 14 data sets.

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Advances in Computing Science — ASIAN 2000: 6th Asian Computing Science Conference Penang, Malaysia, November 25–27, 2000 Proceedings by Jean Vuillemin (auth.), He Jifeng, Masahiko Sato (eds.)


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