RISE is a well-known multi-strategy learning algorithm that combines rule induction and instance-based learning. It achieves higher accuracy than some state-of-the-art learning algorithms, but for large data sets it h...
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We present the WebComposer tool for the automatic composition and execution of Web service-based workflows. We use ontologies to describe and browse workflows. We associate messages and operations with workflow domain...
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We present the WebComposer tool for the automatic composition and execution of Web service-based workflows. We use ontologies to describe and browse workflows. We associate messages and operations with workflow domain concepts using WSDL extensibility. The automatic workflow implementation through WebComposer enables the full separation of the workflow logic and the implementation technology. WebComposer provides the execution of ad-hoc programs by users and the automatic maintenance of these programs, as the available Web services are altered.
The semantic Web technology and the Web services description language extensibility may be combined to describe services in an unambiguous and machine interpretable way, automating Web services discovery, selection an...
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The semantic Web technology and the Web services description language extensibility may be combined to describe services in an unambiguous and machine interpretable way, automating Web services discovery, selection and invocation. In this paper, we present an algorithm and a prototype for the automatic composition of Web services that implement workflows described in a high level language. Our approach has many advantages comparing to the manual creation of a simple program composition, such as smaller implementation time and cost, reliability with the generation of contingency plans, greater capacity to evolve with the dynamic service discovery, and faster execution time with the use of heuristics. We use the OWLS ontology to semantically describe Web services metadata and indexes to help selecting them. The proposed algorithm considers that equivalent services may have different interfaces and also respects preferences of the users.
We present the fuzzy Markov predictor (FMP), a hybrid system that is applied to the task of monthly electric load forecasting. The FMP is a modification we introduce in the hidden Markov model in order to enable it to...
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We present the fuzzy Markov predictor (FMP), a hybrid system that is applied to the task of monthly electric load forecasting. The FMP is a modification we introduce in the hidden Markov model in order to enable it to predict numerical values. The FMP can be seen as an extension of the fuzzy Bayes predictor (FBP) that was modified from the naive Bayes classifier. For verifying the efficiency of the FMP's prediction, we compare it with the FBP, one fuzzy system and two traditional forecasting methods, Box-Jenkins and Winters exponential smoothing.
We present the fuzzy Bayes predictor (FBP), a hybrid system for the task of monthly electric load forecasting. The FBP is a modification we introduce in the naive Bayes classifier in order to enable it to predict nume...
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ISBN:
(纸本)0780370449
We present the fuzzy Bayes predictor (FBP), a hybrid system for the task of monthly electric load forecasting. The FBP is a modification we introduce in the naive Bayes classifier in order to enable it to predict numerical values. We consider three versions of the FBP, each one with a different dependence among the input data: independence, first-order and second-order dependence. For verifying the efficiency of the FBP's prediction, we compare it with two fuzzy systems and two traditional forecasting methods, Box-Jenkins and Winters exponential smoothing.
Proposes a method for data clustering in a n-dimensional space using the elastic net algorithm which is a variant of the Kohonen topographic map learning algorithm. The elastic net algorithm is a mechanical metaphor i...
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Proposes a method for data clustering in a n-dimensional space using the elastic net algorithm which is a variant of the Kohonen topographic map learning algorithm. The elastic net algorithm is a mechanical metaphor in which an elastic ring is attracted by points in a bi-dimensional space while their internal elastic forces try to shun the elastic expansion. The different weights associated with these two kinds of forces lead the elastic to a gradual expansion in the direction of the bi-dimensional points. In this method, the elastic net algorithm is employed with the help of a heuristic framework that improves its performance for application in the n-dimensional space of cluster analysis. Tests were made with two types of data sets: (1) simulated data sets with up to 1000 points randomly generated in groups linearly separable with up to dimension 10 and (2) the Fisher Iris Plant database, a well-known database referred to in the pattern recognition literature. The advantages of the method presented are its simplicity, its fast and stable convergence, beyond efficiency in cluster analysis.
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