Download e-book for kindle: Advances in Computing Science — ASIAN 2000: 6th Asian by Jean Vuillemin (auth.), He Jifeng, Masahiko Sato (eds.)

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

ISBN-10: 3540414282

ISBN-13: 9783540414285

The Asian Computing technological know-how convention (ASIAN) sequence was once initiated in 1995 to supply a discussion board for researchers in laptop technology 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 technological know-how sequence of Springer-Verlag. This year’s convention (ASIAN2000) attracted sixty one submissions from which 18 papers have been chosen via an digital software committee (PC) assembly. the subjects for this year’s convention are: – Logics in machine technology – info Mining – Networks and function the major notice 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 through the Asian Institute of know-how (Thailand), INRIA (France), the nationwide college of Singapore (Singapore), and UNU/IIST (Macau SAR, China). We thank these types of associations for his or her endured help of the ASIAN sequence. This year’s convention should be held in Penang, Malaysia. we're a lot obliged to Universiti Sains Malaysia and Penang country govt for offering the convention venue and to Dr. Abdullah Zawawi Haji Talib for making the neighborhood preparations. We additionally desire to thank the computer participants and the massive variety of referees for thesubstantialworkputinbytheminassessingthesubmittedpapers.

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

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100 or more) attributes. , checking 2100 or more sets). We make DeEPs efficient and scalable for high dimensional data by the following data reduction and concise data-representation techniques. – We reduce the training data sets firstly by removing those items that do not occur in the testing instance and then by selecting the maximal ones from the processed training instances. This data reduction process makes the training data sparser in both horizontal and vertical directions. – We use borders, two-bound structures like , to succinctly represent all EPs contained in a testing instance.

Definition 10. [9,6] A collection C of sets is said to be a convex space if, for all X, Y and Z, the conditions X ⊆ Y ⊆ Z and X, Z ∈ C imply that Y ∈ C. If a collection is a convex space, we say it holds convexity or it is interval closed. Example 5. All of the sets {1}, {1, 2}, {1, 3}, {1, 4}, {1, 2, 3}, and {1, 2, 4} form a convex space. The set L of all the most general elements in this space is {{1}}; the set R of all the most specific elements in this space is {{1, 2, 3}, {1, 2, 4}}. All the other elements can be considered “between” L and R.

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.

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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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