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Publications of year 1983

Books and proceedings

  • David Sankoff and Joseph B. Kruskal. Time Warps, String Edits, and Macromolecules: The Theory and Practice of Sequence Comparison. 1983.
    Keywords: Clustering, Similarity Measures, Time Warping, Sequential/Temporal Data.
    Abstract: Chapter Headings: An Overview of Sequence Comparison, Recognition of Patterns in Genetic Sequences, Fast Algorithms to Determine RNA Secondary Structures Containing Multiple Loops, The Symmetric Time-Warping Problem: From Continuous to Discrete, Use of Dynamic Programming in a Syllable-Based Continuous Speech Recognition System, Application of Sequence Comparison to the Study of Bird Songs, On the Complexity of the Extended String-to-String Correction Problem, An Analysis of the Extenden Tree-Editing Problem, Simultaneous Comparison of Three or More Sequences Related by a Tree, An Anthology of Algorithms and Concepts for Sequence Comparison, Dissimilarity Measure for Clustering Strings, Recent Results on the Complexity of Common-Subsequences Problems, Formal-Language Error Correction, How to Compute String-Edit Distances Quickly, An Upper-Bound Technique for Lengths of Common Subsequences, Probabilistic Behaviour of Longest-Common-Subsequence Length, Common Subsequences and Monotone Subsequences

Articles in journal or book's chapters

  • James F. Allen. Maintaining knowledge about temporal intervals. CACM, 26(11):832--843, 1983.

Conference's articles

  • Usama M. Fayyad and Keki B. Irani. Multi-Interval Discretization of Continuous-Valued Attributes for Classification Learning. In IJCAI93, Chambery, France, pages 1022-1027, 1983.
    Keywords: Discretization, Classification, Decision Trees.
    Abstract: Since most real-world applications of classifcation learning involve continuous-valued attributes, properly addressing the discretization process is an important problem. This paper addresses the use of the entropy minimization heuristic for discretizing the range of a continuous-valued attribute into multiple intervals. We briefly present theoretical evidence for the appropriateness of this heuristic for use in the binary discretization algorithm used in ID3, C4, CART, and oher learning algorithms. The results serve to justify extending the algorithm to derive multiple intervals. We formally derive a criterion based on the minimum description length principle for deciding the partitioning of intervals. We demonstrate via empirical evaluation on several real-world data sets that better decision trees are obtained using the new multi-interval algorithm.

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