By Bikramjit Banerjee, Jing Peng (auth.), Tapio Elomaa, Heikki Mannila, Hannu Toivonen (eds.)
This e-book constitutes the refereed preceedings of the thirteenth ecu convention on desktop studying, ECML 2002, held in Helsinki, Finland in August 2002.
The forty-one revised complete papers provided including four invited contributions have been conscientiously reviewed and chosen from quite a few submissions. one of the themes lined are computational discovery, seek recommendations, type, help vector machines, kernel tools, rule induction, linear studying, determination tree studying, boosting, collaborative studying, statistical studying, clustering, instance-based studying, reinforcement studying, multiagent studying, multirelational studying, Markov selection strategies, energetic studying, etc.
Read Online or Download Machine Learning: ECML 2002: 13th European Conference on Machine Learning Helsinki, Finland, August 19–23, 2002 Proceedings PDF
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Additional info for Machine Learning: ECML 2002: 13th European Conference on Machine Learning Helsinki, Finland, August 19–23, 2002 Proceedings
Example text
References 1. E. Bradley, M. Easley, and R. Stolle. Reasoning about nonlinear system identification. Artificial Intelligence, 133:139–188, 2001. 20 2. H. L. Chan and D. Sutanto. A new battery model for use with battery energy storage systems and electric vehicle power systems. In Proceedings of the IEEE Power Engineering Society Winter Meeting Conference, 2000. 13 22 Stephen D. Bay et al. 3. J. S. Hojnicki, R. D. Green, T. W. Kerslake, D. B. McKissock, and J. J. Trudell. Space station freedom electrical performance model.
We present here two variations of scoring functions that provide some trade-off between the computational cost and the global optimality. 1, which contains a linear number of splits, and let S ∗ be the optimal element in π. We define the cascade function C(f1 , f2 , . . , fn ) as a function which returns f1 if f1 > 0 and C(f2 , . . , fn ) otherwise, having C() = −1. Best Split Scoring. The optimal split is considered for the scoring. The function is composed of three classifiers. e. clauses that do not embed other clauses.
A chunk window of context size n anchored in the word wi codifies the chunk containing the word wi , the previous n chunks and the following n chunks. For each chunk in the window, a feature is formed with the chunk tag and the distance to the central chunk. Patterns. A pattern represents the structure of a sentence fragment which is relevant for distinguishing clauses. , :) and coordinate conjunctions; b) The word “that”; c) Relative pronouns; d) Verb phrases chunks; and e) CLAUSE constituents, already recognized.
