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The two-volume set LNAI 8467 and LNAI 8468 constitutes the refereed lawsuits of the thirteenth overseas convention on man made Intelligence and tender Computing, ICAISC 2014, held in Zakopane, Poland in June 2014. The 139 revised complete papers provided within the volumes, have been rigorously reviewed and chosen from 331 submissions. The sixty nine papers incorporated within the first quantity are fascinated by the subsequent topical sections: Neural Networks and Their purposes, Fuzzy structures and Their purposes, Evolutionary Algorithms and Their purposes, class and Estimation, computing device imaginative and prescient, snapshot and Speech research and designated consultation three: clever equipment in Databases. The seventy one papers within the moment quantity are geared up within the following topics: info Mining, Bioinformatics, Biometrics and clinical purposes, Agent platforms, Robotics and regulate, man made Intelligence in Modeling and Simulation, quite a few difficulties of man-made Intelligence, unique consultation 2: laptop studying for visible info research and safety, specific consultation 1: functions and homes of Fuzzy Reasoning and Calculus and Clustering.
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Additional resources for Artificial Intelligence and Soft Computing: 13th International Conference, ICAISC 2014, Zakopane, Poland, June 1-5, 2014, Proceedings, Part II
Tw/~ cjlin/libsvm 7. : Bayes theorem and information gain based feature selection for maximizing the performance of classiﬁers. , Nagamalai, D. ) CCSIT 2011, Part I. CCIS, vol. 131, pp. 501–511. Springer, Heidelberg (2011) 8. : BRIEF: Binary robust independent elementary features. , Paragios, N. ) ECCV 2010, Part IV. LNCS, vol. 6314, pp. 778–792. Springer, Heidelberg (2010) 9. html 14 P. Artiemjew and P. G´ orecki 10. : Representations of keypoint-based semantic concept detection: A comprehensive study.
4. CV − 10 - result of experiments for VOC data set with 250 considered visual words and SV M classiﬁcation for chi square kernel Visual Dictionary Pruning Using M I and IG 5 13 Conclusions This work is the continuation of the paper , which show us the eﬀectiveness of the way we extended classic feature selection methods. In this paper we have shown four novel methods of visual words selection. A series of experiments have proven the eﬀectiveness of our methods, it has also been shown that our methods are comparable or even better than classic algorithms based on Information Gain and Mutual Information.
Feature selection based on information gain. International Journal of Innovative Technology and Exploring Engineering (IJITEE) 2(2) (2013) 5. : SURF: Speeded up robust features. , Pinz, A. ) ECCV 2006, Part I. LNCS, vol. ECCV, pp. 404–417. Springer, Heidelberg (2006) 6. : LIBSVM: A library for support vector machines. tw/~ cjlin/libsvm 7. : Bayes theorem and information gain based feature selection for maximizing the performance of classiﬁers. , Nagamalai, D. ) CCSIT 2011, Part I. CCIS, vol. 131, pp.