By Weizeng Gao, Zhou Yu, Guoyi Miao (auth.), Hepu Deng, Duoqian Miao, Fu Lee Wang, Jingsheng Lei (eds.)
This e-book constitutes, including LNAI 7002, LNAI 7003, and LNAI 7004, the refereed lawsuits of the foreign convention on man made Intelligence and ComputationaI Intelligence, AICI 2011, held in Taiyuan, China, in September 2011. The 265 revised complete papers provided within the 4 volumes have been conscientiously reviewed and chosen from 1073 submissions. The eighty three papers awarded during this quantity are prepared in topical sections on purposes of man-made intelligence; purposes of computational intelligence; computerized challenge fixing; mind models/cognitive technology; information mining and information gaining knowledge of; specialist and selection help platforms; fuzzy good judgment and tender computing; clever brokers and platforms; clever keep watch over; clever snapshot processing; clever scheduling; clever sign processing; normal language processing; nature computation; neural computation; trend popularity; tough set theory.
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Extra resources for Emerging Research in Artificial Intelligence and Computational Intelligence: International Conference, AICI 2011, Taiyuan, China, September 23-25, 2011. Proceedings
E. the pattern is classified into the class having the most votes over all T trees in the forest. 5. It yields generalization error rate that compares favorably to Adaboost, yet is more robust to noise [15, 16]. Compared with other ensemble classifiers, random forests has the advantages as follows: the variance reduction achieved through averaging over learners and randomized stages decreasing correlation between distinctive learners in the ensemble. 1 Experimental Datasets In this section, we present an experimental evaluation of the random forests technique for the credit card risk prediction.
2. 05 0 Oct Nov Dec Jan Feb Mar Fig. 2. edu From figure 2, we know that the maintainability form Oct to Jan is average, Feb is the minimal and Mar is a value peak, the analysis about the tendency as following. We know the time form Oct to Jan is normal time of the university, the information updating is gently. Feb is Chinese traditional festival “spring festival” in 2011, all of staff and students are in vacation, so that little information is updated. That means the maintainability must be the lowest ebb.
03 From the table we know that the maintainability of business websites is obviously higher than education ones’ a lot. The result is consistent with fact. cn is selected to the tendency analysis from 2010 Oct. to 2011 Mar. The test points also are the last day of every month. Test result is illustrated in Fig. 2. 05 0 Oct Nov Dec Jan Feb Mar Fig. 2. edu From figure 2, we know that the maintainability form Oct to Jan is average, Feb is the minimal and Mar is a value peak, the analysis about the tendency as following.