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Titlebook: Artificial Intelligence: Methodology, Systems, and Applications; 11th International C Christoph Bussler,Dieter Fensel Conference proceeding

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11#
發(fā)表于 2025-3-23 13:38:57 | 只看該作者
Planning and Monitoring Web Service Compositione development, integration, and maintenance of complex services. In this paper, we exploit techniques based on the “Planning as Model Checking” approach to automatically compose web services and synthesize monitoring components. By relying on such a flexible technology, we are able to deal with the
12#
發(fā)表于 2025-3-23 13:59:53 | 只看該作者
13#
發(fā)表于 2025-3-23 19:27:34 | 只看該作者
14#
發(fā)表于 2025-3-23 23:18:06 | 只看該作者
Orthogonal Operators for User-Defined Symbolic Periodicitiesoperty, we introduce a language operator and, this way, we propose a family of symbolic languages, one for each ., one for each point in the lattice. So, the expressiveness and meaning of each operator, and thus of each language in the family, are clearly defined, and a user can select the language
15#
發(fā)表于 2025-3-24 02:21:34 | 只看該作者
An Oracle Based Meta-learner for Function Decompositionefinitions. Though it is effective in discovering the concept structure hidden in the training data, it suffers much from under sampling. In this paper, we propose an oracle based meta learning method that generates new examples with the help of a bagged ensemble to induce accurate classifiers when
16#
發(fā)表于 2025-3-24 09:25:58 | 只看該作者
Bagged Voting Ensembles examined their sufficiency in ensembles. Although, many methods of ensemble creation have been proposed, there is as yet no clear picture of which method is best. In this work, we propose Bagged Voting using different subsets of the same training dataset with the concurrent usage of a voting method
17#
發(fā)表于 2025-3-24 12:29:55 | 只看該作者
18#
發(fā)表于 2025-3-24 14:54:45 | 只看該作者
19#
發(fā)表于 2025-3-24 23:00:48 | 只看該作者
20#
發(fā)表于 2025-3-24 23:28:20 | 只看該作者
Towards a Semantic Representation of Documents by Ontology-Document Mappingtween these concepts and then to disambiguate them. The result is a set of scored concepts-senses (nodes) with weighted links called . which best represents the semantic content of the document. We regard the proposed and evaluated approach as a short but strong step toward the long term goal of Intelligent Indexing and Semantic Retrieval.
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