By Zhixin Ma, Zhe Ding, Yusheng Xu (auth.), Yinglin Wang, Tianrui Li (eds.)

ISBN-10: 3642256635

ISBN-13: 9783642256639

ISBN-10: 3642256643

ISBN-13: 9783642256646

Proceedings of the 6th overseas convention on clever approach and data Engineering offers chosen papers from the convention ISKE 2011, held December 15-17 in Shanghai, China. This court cases doesn’t simply learn unique examine and techniques within the wide components of clever structures and information engineering, but in addition current new methodologies and practices in clever computing paradigms. The publication introduces the present clinical and technical advances within the fields of synthetic intelligence, desktop studying, development acceptance, information mining, details retrieval, knowledge-based structures, wisdom illustration and reasoning, multi-agent structures, natural-language processing, and so forth. moreover, new computing methodologies are provided, together with cloud computing, carrier computing and pervasive computing with conventional clever equipment.
The lawsuits can be precious for either researchers and practitioners who are looking to make the most of clever equipment of their particular study fields.
Dr. Yinglin Wang is a professor on the division of laptop technological know-how and Engineering, Shanghai Jiao Tong collage, China; Dr. Tianrui Li is a professor on the college of data technology and know-how, Southwest Jiaotong collage, China.

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Extra resources for Foundations of Intelligent Systems: Proceedings of the Sixth International Conference on Intelligent Systems and Knowledge Engineering, Shanghai, China, Dec 2011 (ISKE2011)

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In order to make impact of pool size as small as possible and consider distribution of data in original pool simultaneously, we propose a novel pool-based active learning method with query construction CQBP. CQBP constructs representative data of original pool based on selected data. Firstly, active learning classifier selects representative data from current pool as initial state, then changes one property value of initial data each time, without changing categories property and this produces a data set that including initial data.

Information about these data sets is listed in Table 1. In our experiment, each data set is divided into three disjoint subsets, 2 of it is initial training set, 25 percents is testing set, and the remaining is unlabeled set, that is pool in active learning process. Experiments are conducted on the UCI Machine Learning Repository [9]. An ensemble of 100 j48 decision trees Bagging (implemented in Weka) is used as active learner in this paper. We repeat the experiment 20 times, 50 runs for each data set, and record average prediction error rate of classifiers on test data set.

Fuzzy neural network technology also it can be applied to sintering process of material layer in the modeling of permeability index. This paper introduces fuzzy neural network based on the subtraction clustering type of adaptive neural fuzzy reasoning system. Based on the data modeling method is the biggest characteristic of it [4]. Fuzzy membership functions and the fuzzy rules of the system are not based on experience but it is known by learning. Numerical clustering 26 J. Teng and X. Zhang analysis is the base of classification and system modeling.

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Foundations of Intelligent Systems: Proceedings of the Sixth International Conference on Intelligent Systems and Knowledge Engineering, Shanghai, China, Dec 2011 (ISKE2011) by Zhixin Ma, Zhe Ding, Yusheng Xu (auth.), Yinglin Wang, Tianrui Li (eds.)


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