By De-Shuang Huang, Kang-Hyun Jo, Ling Wang
This ebook – along with the volumes LNCS 8588 and LNBI 8590 – constitutes the refereed court cases of the tenth foreign convention on clever Computing, ICIC 2014, held in Taiyuan, China, in August 2014. The eighty five papers of this quantity have been rigorously reviewed and chosen from quite a few submissions. The papers are geared up in topical sections corresponding to delicate computing; synthetic bee colony algorithms; unsupervised studying; kernel equipment and assisting vector machines; computing device studying; fuzzy thought and algorithms; picture processing; clever computing in machine imaginative and prescient; clever computing in verbal exchange networks; clever image/document retrievals; clever facts research and prediction; clever agent and net purposes; clever fault analysis; wisdom representation/reasoning; wisdom discovery and knowledge mining; average language processing and computational linguistics; subsequent gen sequencing and metagenomics; clever computing in scheduling and engineering optimization; complex modeling, keep watch over and optimization concepts for advanced engineering platforms; complicated networks and their functions; time sequence forecasting and research utilizing synthetic neural networks; laptop human interplay utilizing a number of visible cues and clever computing; biometric approach and defense for clever computing.
Read or Download Intelligent Computing Methodologies: 10th International Conference, ICIC 2014, Taiyuan, China, August 3-6, 2014. Proceedings PDF
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Additional resources for Intelligent Computing Methodologies: 10th International Conference, ICIC 2014, Taiyuan, China, August 3-6, 2014. Proceedings
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Optimization and approximation in deterministic sequencing and scheduling: a survey. Annals of Discrete Mathematics 5, 287–326 (1979) 12. : Benchmarks for basic scheduling problems. European Journal of Operational Research 64, 278–285 (1993) 13. : A faster algorithm for calculating hypervolume. IEEE Transactions on Evolutionary Computation 10(1), 29–38 (2006) 14. : Indicator-based selection in multiobjective search. , et al. ) PPSN VIII. LNCS, vol. 3242, pp. 832–842. Springer, Heidelberg (2004) 15.
J. Franklin Inst. : A New Continuous Action-Set Learning Automaton for Function Optimization. au Abstract. This paper introduces a novel 2-stage classification system with stacking and genetic algorithm (GA) based feature selection. Specifically, Level1 data is first generated by stacking on the original data (called Level0 data) with base classifiers. Level1data is then classified by a second classifier (denoted by C) with feature selection using GA. The advantage of applying GA on Level1 data is that it has lower dimension and is more uniformity than Level0 data.
The mapping f defines the evaluaX, and often one is interested in those solutions that are Pareto tion of a solution x optimal with respect to f. The relation x1 x2 means that the solution x1 is preferable to x2. The dominance relation between two solutions x1 and x2 is usually defined as follows [14]: Definition 1. A decision vector x1 is said to dominate another decision vector x2 (writfi(x2) for all i {1, , n} and fj(x1) < fj(x2) for at least ten as x1 ≻ x2), if fi(x1) one j {1, , n}. Definition 2.