Download Artificial Intelligence: Methodology, Systems, and by David House, Björn Granström (auth.), Donia Scott (eds.) PDF

By David House, Björn Granström (auth.), Donia Scott (eds.)

This e-book constitutes the refereed court cases of the tenth overseas convention on man made Intelligence: technique, platforms, and Appliations, AIMSA 2002, held in Varna, Bulgaria in September 2002.
The 26 revised complete papers offered including 2 invited papers have been conscientiously reviewed and chosen for inclusion during this ebook. The papers deal with a vast spectrum of issues in AI, together with typical language processing, computational studying, desktop studying, AI making plans, heuristics, neural info processing, adaptive platforms, computational linguistics, multi-agent platforms, AI good judgment, wisdom administration, and data retrieval.

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Additional info for Artificial Intelligence: Methodology, Systems, and Applications: 10th International Conference, AIMSA 2002 Varna, Bulgaria, September 4–6, 2002 Proceedings

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J. van den Herik worst-case complexities of the representation and its algorithms given in table 1. An analysis of these complexities shows that each of them is equal to the sum of the corresponding complexities of the IBMBS and IBmBS. Thus, the IBMBS and the IBmBS have two advantages: (1) they are more efficiently computable than the IBBS, and (2) the IBMBS and the IBmBS have more efficient algorithms for learning, retraction, version-space collapse, and classification. 8 Conclusion This paper introduces the IBMBS and the IBmBS as new version-space representations.

The right side shows the partitioning that this tree creates in the attribute space. A decision tree example is shown in Figure 3, together with its 2-D related space. Each node of the tree tests a linear combination of some indices (equivalent to an hyperplane) and each leaf of the tree corresponds to a class. The main advantages of OC1 is that it finds smaller decision trees than classical tree learning methods. The major drawback is that they are less readable than classical ones. The classification of a new loop is equivalent to finding a leaf loop class.

Corollary 17 states that if the concept language is admissible such that the property IP holds, then none of the descriptions of a version space VS (I + , I − ) covers an instance i ∈ I iff there exists a set G(I + , {n}) of which all the descriptions do not cover the instance i. Corollary 17. If the concept language Lc is admissible such that the property IP holds, and I − = ∅, then: (∀i ∈ I)((∀c ∈ VS (I + , I − ))¬M (c, i) ↔ (∃n ∈ I − )(∀g ∈ G(I + , {n}))¬M (g, i)). N. G. J. van den Herik Classify Algorithm Input: i: an instance to be classified.

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