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 commercial Engineering and different functions of utilized Intelligend structures, IEA/AIE 2012, held in Dalian, China, in June 2012. the whole of eighty two papers chosen for the complaints have been conscientiously reviewed and chosen from various submissions. The papers are geared up in topical sections on laptop studying equipment; cyber-physical method for clever transportation purposes; AI purposes; evolutionary algorithms, combinatorial optimization; modeling and help of cognitive and affective human tactics; average language processing and its purposes; social community and its functions; mission-critical purposes and case stories of clever structures; AI tools; sentiment research for asian languages; features on cognitive computing and clever interplay; spatio-temporal datamining, established studying and their functions; selection making and information dependent platforms; development attractiveness; agent dependent structures; determination making ideas and leading edge wisdom administration; desktop studying applications.
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Extra 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
Grouping ﬁltering does not dispose of all noise; it smoothes noise. Grouping ﬁltering can get several feature values and expresses more ambiguity and detecte harmful or safe documents without setting parameters. Grouping ﬁltering method provides a ﬁne setting and is a practical classiﬁer. In future work, we will verify co-occurrence ﬁltering by using more data sets. When the training data are insuﬃcient, we must improve the accuracy. References 1. html 2. : A plan for spam. In: Graham, P. ) Hackers and Painters.
Ito Creating Databases We created two kinds of databases for Bayesian and co-occurrence ﬁltering. In the former, the database was created by word frequency. In the latter, it was created by a pair or more of words. Figure 2 shows the ﬂow of creating a database. First, the morphemes of the datasets are analyzed. This paper used MeCab, which is a Japanese morphological analyzer. Step 1 creates a database that has two ﬁelds which are a word and an id. This database is called the word index. Step 2 separately counts the frequency of each word in harmful or safe documents.
The intuition behind Method II is to proceed incrementally and accept only a group of most confident classifications at each step. These classifications may provide useful information for the following iterations. Therefore, the products classified at each step are added to the training set. At the end of each step, term frequencies and price statistics are updated for all categories. As a result, unclassified product data from an unknown store is integrated into the training set, gaining more information about new prices and partially learning the vocabulary of the source store.