Titlebook: Advances in Soft Computing; 9th Mexican Internat Grigori Sidorov,Arturo Hernández Aguirre,Carlos Al Conference proceedings 2010 Springer Be

Malcontent

Vladimir Estivill-Castro,Derick Woode from the Bayesian network. Through the use of these unlabeled nodes, we are able to restructure these trees, and reduce the amount of computation performed during the inference process. Empirical tests show that the algorithm can reduce multiplications by up to 70%, and overall runtime by up to 50

使迷惑

https://doi.org/10.1007/978-3-642-03351-3 page faults. Plenty of techniques are available to devise strong prediction mechanism for prefetching but they either are situation specific (Locality of reference principle) or inadaptable (Markovian model) and costly. We have proposed a generic and adaptable technique benefiting from past experie

終端

Elkan’s k-Means Algorithm for Graphsrading computational time against solution quality, we avoid unnecessary graph distance calculations by exploiting the triangle inequality of the underlying distance metric. In experiments we show that the accelerated k-means for graphs is faster than k-means for graphs provided there is a cluster structure in the data.

阻礙

Methods and Algorithms of Information Generalization in Noisy Databasesise is considered. Noise models are presented, and a noise effect on the operation of generalization algorithms using the methods of building decision trees and forming production rules is developed. The results of program modeling are brought about.

gregarious

Recognizing Textual Entailment Using a Machine Learning Approacho classify the entailment pairs we use a symmetric similarity measure and a non-symmetric similarity measure. Our system achieved an accuracy of 66% on the RTE-3 development dataset (with 10-fold cross validation) and accuracy of 63% on the RTE-3 test dataset.

遺傳學(xué)

設(shè)想

Advances in Soft Computing978-3-642-16773-7Series ISSN 0302-9743 Series E-ISSN 1611-3349

過剩

在駕駛

Lecture Notes in Computer Scienceise is considered. Noise models are presented, and a noise effect on the operation of generalization algorithms using the methods of building decision trees and forming production rules is developed. The results of program modeling are brought about.

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