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A granularity attribute reduction method based on binary discernibility matrix

International Journal of Advancements in computing Technology 2012年第12期4卷 213-221页

作者： Chang, Tong Shifei, Ding Hong, Zhu Hongjie, Jia Jian, Wang School of Computer Science and Technology China University of Mining and Technology Xuzhou 221116 China Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences Beijing 100190 China Geomatics College Shandong University of Science and Technology Qingdao 266510 China

At present, most of the attribute reduction algorithms based on granularity are simply computing the granularity of knowledge. Repeated calculation will increase the time complexity. Binary discernibility matrix is used to express binary form, which has a clear ascension whether in space or in time than the traditional discernibility matrix efficiency. On the basis of granularity-based attribute reduction, a method is proposed to preprocess the dataset by using binary discernibility matrix. Firstly, find the core attribute and a minimal reduction. Then use the granularity thought to calculate each particle of the importance of attributes. Most important is joined to the reduction set, thereby achieving the attributes reduction. The example analysis shows that the method can improve the performance of the traditional attribute reduction algorithms effectively. It is a feasible approach to reduce attributes.

关键词： Rough set theory

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A support vector extraction method based on clustering membership

International Journal of Digital Content Technology and its ...

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International Journal of Digital Content Technology and its Applications 2012年第13期6卷 1-10页

作者： Qi, Bingjuan Ding, Shifei Huang, Huajuan Yu, Junzhao Wang, Jian School of Computer Science and Technology China University of Mining and Technology Xuzhou 221116 China Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Science Beijing 100190 China Geomatics College Shandong University of Science and Technology Qingdao 266510 China

Traditional support vector machine has disadvantages of slow training speed and great time consumption when dealing with large-scale datasets. This paper proposes a support vector extraction method based on clustering membership, which preprocesses the training datasets and extracts all possible support vectors for SVM training according to the memberships. Considering the training datasets may be linear or nonlinear, this paper severally uses FCM and KFCM to extract support vectors. Experiment results show that the method proposed in this paper can improve the training speed greatly in the condition of maintaining the classification accuracy.

关键词： Support vector machines

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Image sets alignment for Video-Based Face Recognition

Image sets alignment for Video-Based Face Recognition

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Conference on Computer Vision and Pattern Recognition (CVPR)

作者： Zhen Cui Shiguang Shan Haihong Zhang Shihong Lao Xilin Chen School of Computer Science and Technology Huaqiao University Xiamen China Key Laboratory of Intelligent Information Processing of Chinese Academy of Sciences (CAS) Institute of Computing Technology CAS Beijing China Omron Social Solutions Company Limited Kyoto Japan

Video-based Face Recognition (VFR) can be converted to the matching of two image sets containing face images captured from each video. For this purpose, we propose to bridge the two sets with a reference image set that is well-defined and pre-structured to a number of local models offline. In other words, given two image sets, as long as each of them is aligned to the reference set, they are mutually aligned and well structured. Therefore, the similarity between them can be computed by comparing only the corresponded local models rather than considering all the pairs. To align an image set with the reference set, we further formulate the problem as a quadratic programming. It integrates three constrains to guarantee robust alignment, including appearance matching cost term exploiting principal angles, geometric structure consistency using affine invariant reconstruction weights, smoothness constraint preserving local neighborhood relationship. Extensive experimental evaluations are performed on three databases: Honda, MoBo and YouTube. Compared with competing methods, our approach can consistently achieve better results.

关键词： Manifolds Face Image matching Face recognition Image reconstruction Video sequences Computational modeling

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Twin support vector machines based on rough sets

International Journal of Digital Content Technology and its ...

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International Journal of Digital Content Technology and its Applications 2012年第20期6卷 493-500页

作者： Yu, Junzhao Ding, Shifei Jin, Fengxiang Huang, Huajuan Han, Youzhen School of Computer Science and Technology China University of Mining and Technology Xuzhou 221116 China Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Science Beijing 100190 China Beijing Key Laboratory of Intelligent Telecommunications Software and Multimedia Beijing University of Posts and Telecommunications Beijing 100876 China Geomatics College Shandong University of Science and Technology Qingdao 266510 China

TWSVM(Twin Support Vector Machines) is based on the idea of GEPSVM (Proximal SVM based on Generalized Eigenvalues), which determines two nonparallel planes by solving two related SVM-type problems, so that its computing cost in the training phase is 1/4 of standard SVM. In addition to keeping the superior characteristics of GEPSVM, the classification performance of TWSVM significantly outperforms that of GEPSVM. In order to further improve the speed and accuracy of TWSVM, this paper proposes the twin support vector machines based on rough sets. Firstly, using the rough sets theory to reduce the attributes, and then using TWSVM to train and predict the new datasets. The final experimental results and data analysis show that the proposed algorithm has higher accuracy and better efficiency compared with the traditional twin support vector machines.

关键词： Rough set theory

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The study on power quality evaluation based on data analysis

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International Journal of Advancements in computing Technology 2012年第19期4卷 586-592页

作者： Zhang, Li Jiang, Yongcheng Liu, Ning Zhang, Bao-Hua Key Laboratory of Intelligent Computing and Signal Processing Ministry of Education Anhui University Hefei 230601 China School of Electric Engineering and Automation Anhui University Hefei 230601 China Library of Hefei Normal University Hefei 230601 China School of Electronics and Information Engineering Anhui University Hefei 230601 China

Power quality is the guide to the power of equipment malfunction or cannot work normally voltage. Rapid use, to the electric power quality caused serious, destruction and pollution power quality, produce the voltage wave and so on many kinds of power quality problems, to the user and power supply party bring huge economic loss "at the same time to plan. Microprocessor as the core of the intelligent precision equipment to the electric power quality put forward higher requirement, how to effectively can quality problem, improve the quality of power become the electric power industry research hotspot and focus of power quality inspection Richard and improve the quality of power, the prerequisite and basis of only fast. The correct detection and recognition of the electrical energy to take the right management measures improve the quality of power;reduce the loss of power quality problems.

关键词： Power quality

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Relaxed cross-lingual projection of constituent syntax

Relaxed cross-lingual projection of constituent syntax

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Conference on Empirical Methods in Natural Language processing, EMNLP 2011

作者： Jiang, Wenbin Liu, Qun Lü, Yajuan Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences China

ISBN: (纸本)1937284115

We propose a relaxed correspondence assumption for cross-lingual projection of constituent syntax, which allows a supposed constituent of the target sentence to correspond to an unrestricted treelet in the source parse. Such a relaxed assumption fundamentally tolerates the syntactic non-isomorphism between languages, and enables us to learn the target-language-specific syntactic idiosyncrasy rather than a strained grammar directly projected from the source language syntax. Based on this assumption, a novel constituency projection method is also proposed in order to induce a projected constituent tree-bank from the source-parsed bilingual corpus. Experiments show that, the parser trained on the projected treebank dramatically outperforms previous projected and unsupervised parsers. © 2011 Association for Computational Linguistics.

关键词： Syntactics

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Fast generation of translation forest for large-scale SMT discriminative training

Fast generation of translation forest for large-scale SMT di...

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Conference on Empirical Methods in Natural Language processing, EMNLP 2011

作者： Xiao, Xinyan Liu, Yang Liu, Qun Lin, Shouxun Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences China

ISBN: (纸本)1937284115

Although discriminative training guarantees to improve statistical machine translation by incorporating a large amount of overlapping features, it is hard to scale up to large data due to decoding complexity. We propose a new algorithm to generate translation forest of training data in linear time with the help of word alignment. Our algorithm also alleviates the oracle selection problem by ensuring that a forest always contains derivations that exactly yield the reference translation. With millions of features trained on 519K sentences in 0.03 second per sentence, our system achieves significant improvement by 0.84 BLEU over the baseline system on the NIST Chinese-English test sets. © 2011 Association for Computational Linguistics.

关键词： Forestry

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Editorial

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International Journal on Artificial Intelligence Tools 2012年第3期21卷

作者： Cui, Zhihua Shi, Zhongzhi Alex, Rajan Complex System and Computational Intelligence Laboratory School of Computer Science and Technology Taiyuan University of Science and Technology Shanxi 030024 China Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences No. 6 Kexueyuan South Road Zhongguancun Haidian District Beijing 100190 China Department of Engineering and Computer Science West Texas AandM University Canyon TX 79015 United States

Swarm intelligence is an umbrella for amount optimization algorithms. This discipline deals with natural and artificial systems composed of many individuals that coordinate their activities using decentralized control and self-organization. In general, multi-agent systems that use some swarm intelligence are said to be swarm intelligent systems. They are mostly used as search engines and optimization tools. The goal of this special issue has been to offer a wide spectrum of sample works throughout the world about innovative methodologies of swarm intelligence. The issue should be useful both for beginners and experienced researchers in the field of computational intelligence. © 2012 World Scientific Publishing Company.

关键词： Swarm intelligence

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Effectively Discriminating Fighting Shots in Action Movies

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Journal of Computer Science & Technology 2011年第1期26卷 187-194页

作者：马述高王伟强 School of Information Science and Engineering Graduate University of Chinese Academy of Sciences Computer Science Department Boston UniversityBoston MA 02215U.S.A. Key Laboratory of Intelligent Information Processing Institute of Computing TechnologyChinese Academy of Sciences

Fighting shots are the highlights of action movies and an effective approach to discriminating fighting shots is very useful for many applications, such as movie trailer construction, movie content filtering, and movie content retrieval. In this paper, we present a novel method for this task. Our approach first extracts the reliable motion information of local invariant features through a robust keypoint tracking computation; then foreground keypoints are distinguished from background keypoints by a sophisticated voting process; further, the parameters of the camera motion model is computed based on the motion information of background keypoints, and this model is then used as a reference to compute the actual motion of foreground keypoints; finally, the corresponding feature vectors are extracted to characterizing the motions of foreground keypoints, and a support vector machine （SVM） classifier is trained based on the extracted feature vectors to discriminate fighting shots. Experimental results on representative action movies show our approach is very effective.

关键词： video analysis motion concept learning

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An orientation model for hierarchical phrase-based translation

An orientation model for hierarchical phrase-based translati...

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2011 International Conference on Asian Language processing, IALP 2011

作者： Xiao, Xinyan Su, Jinsong Liu, Yang Liu, Qun Lin, Shouxun Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences Beijing China

ISBN: (纸本)9780769545547

The hierarchical phrase-based (HPB) translation exploits the power of grammar to perform long distance reorderings, without specifying nonterminal orientations against adjacent blocks or considering the lexical information covered by nonterminals. In this paper, we borrow from phrase-based system the idea of orientation model to enhance the reordering ability of HPB translation. We distinguish three orientations (monotone, swap, discontinuous) of a nonterminal based on the alignment of grammar, and select the appropriate orientation of nonterminal using lexical information covered by it. By incorporating the orientation model, our approach significantly outperforms a standard HPB system up to 1.02 Bleu on large scale NIST Chinese-English translation task, and 0.51 Bleu on WMT German-English translation task. © 2011 IEEE.

关键词： Natural language processing systems

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