916 resultados para Discrete Mathematics and Combinatorics


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2000 Mathematics Subject Classification: 62P10.

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2000 Mathematics Subject Classification: 60J80.

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2000 Mathematics Subject Classification: 60J80.

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2000 Mathematics Subject Classification: 60J80.

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Report published in the Proceedings of the National Conference on "Education in the Information Society", Plovdiv, May, 2013

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2000 Mathematics Subject Classification: 60J27.

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2000 Mathematics Subject Classification: 60K15, 60K20, 60G20,60J75, 60J80, 60J85, 60-08, 90B15.

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2000 Mathematics Subject Classification: 60G70, 60G18.

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2000 Mathematics Subject Classification: 68T50.

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2000 Mathematics Subject Classification: 62P10, 92D10, 92D30, 62F03

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In this note we discuss upper and lower bound for the ruin probability in an insurance model with very heavy-tailed claims and interarrival times.

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In this paper, we present an innovative topic segmentation system based on a new informative similarity measure that takes into account word co-occurrence in order to avoid the accessibility to existing linguistic resources such as electronic dictionaries or lexico-semantic databases such as thesauri or ontology. Topic segmentation is the task of breaking documents into topically coherent multi-paragraph subparts. Topic segmentation has extensively been used in information retrieval and text summarization. In particular, our architecture proposes a language-independent topic segmentation system that solves three main problems evidenced by previous research: systems based uniquely on lexical repetition that show reliability problems, systems based on lexical cohesion using existing linguistic resources that are usually available only for dominating languages and as a consequence do not apply to less favored languages and finally systems that need previously existing harvesting training data. For that purpose, we only use statistics on words and sequences of words based on a set of texts. This solution provides a flexible solution that may narrow the gap between dominating languages and less favored languages thus allowing equivalent access to information.

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2000 Mathematics Subject Classification: 60J80, 60J10.

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We build the Conditional Least Squares Estimator of 0 based on the observation of a single trajectory of {Zk,Ck}k, and give conditions ensuring its strong consistency. The particular case of general linear models according to 0=( 0, 0) and among them, regenerative processes, are studied more particularly. In this frame, we may also prove the consistency of the estimator of 0 although it belongs to an asymptotic negligible part of the model, and the asymptotic law of the estimator may also be calculated.

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2000 Mathematics Subject Classification: primary: 60J80, 60J85, secondary: 62M09, 92D40