Advanced Research in Applied Artificial Intelligence: 25th by Qing He, Ye Duan, Danyang Zhang (auth.), He Jiang, Wei Ding,

February 23, 2017 | Research | By admin | 0 Comments

By Qing He, Ye Duan, Danyang Zhang (auth.), He Jiang, Wei Ding, Moonis Ali, Xindong Wu (eds.)

This quantity constitutes the completely refereed convention complaints of the twenty fifth foreign convention on business Engineering and different functions of utilized Intelligend platforms, IEA/AIE 2012, held in Dalian, China, in June 2012. the whole of eighty two papers chosen for the complaints have been rigorously reviewed and chosen from quite a few submissions. The papers are geared up in topical sections on computing device studying tools; cyber-physical procedure for clever transportation purposes; AI purposes; evolutionary algorithms, combinatorial optimization; modeling and help of cognitive and affective human procedures; usual language processing and its functions; social community and its functions; mission-critical functions and case reports of clever platforms; AI tools; sentiment research for asian languages; points on cognitive computing and clever interplay; spatio-temporal datamining, established studying and their functions; determination making and information established platforms; development attractiveness; agent established structures; selection making thoughts and cutting edge wisdom administration; computing device studying applications.

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Additional info for Advanced Research in Applied Artificial Intelligence: 25th International Conference on Industrial Engineering and Other Applications of Applied Intelligent Systems, IEA/AIE 2012, Dalian, China, June 9-12, 2012. Proceedings

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Cooccurrence 2’s F-score is as same as grouping co-occurrence’s F-score, but cooccurence 2 has a larger difference between recall and precision than grouping co-occurence. So, grouping co-occurence is stable as filtering because of balanced result. safe documents harmful documents positive negative 6870 1130 778 7222 Fig. 6. Grouping co-occurrence filtering with Gray Robinson Table 1. 878 38 T. Yoshimura, Y. Fujii, and T. Ito Table 2. 883 Conclusion We gathered datasets to implement the two Filtering, and implemented the Grouping filtering and Co-occurrence filtering.

In the Gray Robinson method, the scope of a feature value is disposed of as a noise word, but grouping co-occurrence filtering disposes of noise by smoothing co-occurence feature values. Table 1 shows the recall, precision, and f-score of the grouping co-occurrence and co-occurrence filtering for detecting safe documents. Grouping co-occurrence filtering was applied, so the f-score was higher than the others. Table 2 shows also three rates for detecting harmful documents. Cooccurrence 2’s F-score is as same as grouping co-occurrence’s F-score, but cooccurence 2 has a larger difference between recall and precision than grouping co-occurence.

Recently, many efforts have been put to transfer learning [7]. Basically, transfer learning resolves the issue that training date and future deployment environment (or so-called testing data) may have different distributions or feature space. Transfer learning does not deal with issues in batched H. Jiang et al. ): IEA/AIE 2012, LNAI 7345, pp. 40–47, 2012. © Springer-Verlag Berlin Heidelberg 2012 Model Fusion-Based Batch Learning with Application to Oil Spills Detection 41 data directly. In this work, we attempt to address this issue and investigate an effective method for batch learning by respecting batched data structure and its characteristics represented by features instead of attributes.

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