Titlebook: Current Topics in Artificial Intelligence; 13th Conference of t Pedro Meseguer,Lawrence Mandow,Rafael M. Gasca Conference proceedings 2010

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书目名称Current Topics in Artificial Intelligence影响因子(影响力)




书目名称Current Topics in Artificial Intelligence影响因子(影响力)学科排名




书目名称Current Topics in Artificial Intelligence网络公开度




书目名称Current Topics in Artificial Intelligence网络公开度学科排名




书目名称Current Topics in Artificial Intelligence被引频次




书目名称Current Topics in Artificial Intelligence被引频次学科排名




书目名称Current Topics in Artificial Intelligence年度引用




书目名称Current Topics in Artificial Intelligence年度引用学科排名




书目名称Current Topics in Artificial Intelligence读者反馈




书目名称Current Topics in Artificial Intelligence读者反馈学科排名

Ballerina

10 Jahre EU-Mitgliedschaft Österreichsver, the specific axioms of our logic have been obtained from the . set of formulas needed in our definition of qualitative sum of small, medium and large numbers. We also present some of the advantages of our approach on the basis of an example.

该得

Zustellung an eine Abgabestelleanti-patterns, and (2) errors not related to existing ODPs, called pitfalls. This classification is the result of analysing a set of ontologies. This paper is focused on the pitfalls identified during the analysis. In addition the paper presents a classification of the pitfalls found and a set of pitfall examples.

正式演说

https://doi.org/10.1007/978-3-211-69918-8ssible to find configurations that select few genes without significantly worsening the error rate of the best classifier. Average ranking for 10 to 40 genes shows that SVM-RFE with Naïve Bayes and FCBF with SVM behave consistently well.

aquatic

A Multi-objective Neuro-evolutionary Algorithm to Obtain Interpretable Fuzzy Modelsms of accuracy, interpretability and compactness by using three test problems studied in literature. Statistical tests have also been used with optimality and diversity multi-objective metrics to compare the schemata.

Asperity

Closeness and Distance Relations in Order of Magnitude Qualitative Reasoning via PDLver, the specific axioms of our logic have been obtained from the . set of formulas needed in our definition of qualitative sum of small, medium and large numbers. We also present some of the advantages of our approach on the basis of an example.

Asperity

Common Pitfalls in Ontology Developmentanti-patterns, and (2) errors not related to existing ODPs, called pitfalls. This classification is the result of analysing a set of ontologies. This paper is focused on the pitfalls identified during the analysis. In addition the paper presents a classification of the pitfalls found and a set of pitfall examples.

languid

Selecting Few Genes for Microarray Gene Expression Classificationssible to find configurations that select few genes without significantly worsening the error rate of the best classifier. Average ranking for 10 to 40 genes shows that SVM-RFE with Naïve Bayes and FCBF with SVM behave consistently well.

隐藏

Contribution of the ECE to SECIte it to the concept of Ackermann constraints. Notably, our empirical results demonstrate that our novel technique considerably increases the diagnosis resolution even under presence of only a couple of test cases.

dry-eye