Titlebook: Intelligent Computing Technology; 8th International Co De-Shuang Huang,Changjun Jiang,Juan Carlos Figuero Conference proceedings 2012 Sprin

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烦忧

摆动

Protein-Protein Binding Affinity Prediction Based on an SVR Ensembleyer to infer the final affinities. Leave-one-out validation on our heterogeneous data shows that the TLSVR method obtains a very good result of R=0.80 and SD=1.32 with real affinities. Comparison experiment further demonstrates that TLSVR is superior to the previous state-of-art methods in predicting generic PPBA.

Chivalrous

0302-9743 ence proceedings of the 8th International Conference on Intelligent Computing, ICIC 2012, held in Huangshan, China, in July 2012. The 242 revised full papers presented were carefully reviewed and selected from 753 submissions. The 84 papers included in this volume are organized in topical sections o

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2D Discontinuous Function Approximation with Real-Valued Grammar-Based Classifier System and unexpected input real values. Performance evaluations show that the additional Simple Accept Radius mechanism enables rGCS to accurately approximate 2D discontinuous function. Performance comparisons with another real-valued LCS show that rGCS yields competitive performance.

灾祸

A Phased Adaptive PSO Algorithm for Multimodal Function Optimizationcaping local stagnation. The illustrative example is provided to confirm the validity, as compared with the SPSO, Dynamic Inertia Weight PSO(PSO-W), and Tradeoff PSO(PSO-T) in terms of convergence speed and the ability of jumping out of the local optimal value. Simulation results confirm that the proposed algorithm is effective and feasible.

别名

The Comparative Study of Different Number of Particles in Clustering Based on Three-Layer Particle Sforming from as little as possible to as much as possible in each swarm when the function dimension is increasing from less to more. Finally, the original algorithm and THLPSO are compared to illustrate the efficiency of the proposed method.

笨拙处理

athlete’s-foot

Study on Web Text Feature Selection Based on Rough Setepending on the term’s importance to classification, and then the paper proposes a feature selection algorithm based on rough set. Experiments show that this method can effectively improve the classification accuracy. It can not only reduce the dimension of feature space, but also improve the accuracy of classification.