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International Journal of Digital Content Technology and its Applications 2012年第20期6卷 484-492页

作者： Huang, Huajuan Ding, Shifei Jin, Fengxiang Yu, Junzhao 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 100080 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

The constaints of time and memory will reduce the learning performance of Support Vector Machine (SVM) when it is used to solve the large number of samples. In order to solve this problem, a novel algorithm called Granular Support Vector Machine based on Mixed Kernel Function (GSVM-MKF) is proposed. Firstly, the granular method is propsed and then the judgment and extraction methods of support vector particles are given. On the above basis, we propose a new granular support vector machine learning model. Secondly, in order to further improve the performance of the granular support vector machine learning model, a mixed kernel function which effectively uses the global kernel function having the good generalization ability and the local kernel function having good learning ability is proposed. Finally, the theoretical analysis and experimental results show the effectiveness of the method.

关键词： Support vector machines

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On the support of ad-hoc semantic web data sharing 1

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7th IFIP International Conference on intelligent information processing, IIP 2012

作者： Zhou, Jing Yang, Kun Shi, Lei Shi, Zhongzhi School of Computer Science Communication University of China Beijing 100024 China Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences Beijing 100190 China

ISBN: (数字)9783642328916

ISBN: (纸本)9783642328909

Sharing Semantic Web datasets provided by different publishers in a decentralized environment calls for efficient support from distributed computing technologies. Moreover, we argue that the highly dynamic ad-hoc settings that would be pervasive for Semantic Web data sharing among personal users in the future pose even more demanding challenges for enabling technologies. We propose an architecture that is based upon the peer-to-peer (P2P) paradigm for ad-hoc Semantic Web data sharing and identify the key technologies that underpin the implementation of the architecture. We anticipate that our (current and future) work will offer powerful support for sharing of Semantic Web data in a decentralized manner and becomes an indispensable and complementary approach to making the Semantic Web a reality. © 2012 IFIP International Federation for information processing.

关键词： Digital storage

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Adaptive associative classification with emerging frequent patterns

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High Technology Letters 2012年第1期18卷 38-44页

作者： Wang Xiaofeng Zhang Dapeng Shi Zhongzhi The Key Laboratory of Intelligent Information Processing Institute of Computing TechnologyChinese Academy of SciencesBeijing 100190P.R.China Graduate University of Chinese Academy of Sciences Beijing 100049P.R.China Institute of Information Science and Engineering Yanshan UniversityQinhuangdao 066004P.R.China

In this paper, we propose an enhanced associative classification method by integrating the dynamic property in the process of associative classification. In the proposed method, we employ a support vector machine（SVM） based method to refine the discovered emerging ~equent patterns for classification rule extension for class label prediction. The empirical study shows that our method can be used to classify increasing resources efficiently and effectively.

关键词： associative classification rule frequent pattern mining emerging frequent pattern supportvector machine （SVM）

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Hierarchical chunk-to-string translation

Hierarchical chunk-to-string translation

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50th Annual Meeting of the Association for Computational Linguistics, ACL 2012

作者： Feng, Yang Zhang, Dongdong Li, Mu Zhou, Ming Liu, Qun Department of Computer Science University of Sheffield Sheffield United Kingdom Microsoft Research Asia China Key Laboratory of Intelligent Information Processing Chinese Academy of Sciences Institute of Computing Technology China

ISBN: (纸本)9781937284244

We present a hierarchical chunk-to-string translation model, which can be seen as a compromise between the hierarchical phrasebased model and the tree-to-string model, to combine the merits of the two models. With the help of shallow parsing, our model learns rules consisting of words and chunks and meanwhile introduce syntax cohesion. Under the weighed synchronous context-free grammar defined by these rules, our model searches for the best translation derivation and yields target translation simultaneously. Our experiments show that our model significantly outperforms the hierarchical phrasebased model and the tree-to-string model on English-Chinese Translation tasks. © 2012 Association for Computational Linguistics.

关键词： Context free grammars

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Mining association rules on qing court medical records: Semantic abstraction and standardization

Mining association rules on qing court medical records: Sema...

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2012 IEEE 12th International Conference on Computer and information Technology, CIT 2012

作者： Cao, Cong Wang, Weimin Cao, Cungen Zang, Liangjun Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences Beijing China Graduate University Chinese Academy of Sciences Beijing China School of Computer Science and Engineering Jiangsu University of Science and Technology Zhenjiang China

ISBN: (纸本)9780769548586

To explore the association relations among disease, pathogenesis, physician, symptoms and drug, we adapt a variational Apriori algorithm for discovering association rules on a dataset of the Qing Court Medical Records. There are five types of semantic associations we intend to discover, including Disease-Pathogenesis-Drug set(DPaD), Disease-Symptoms-Drug set (DSyD), Disease-Drug set (DD), Disease-Physician-Drug set (DPhD) and Disease-Drug Category Set (DDC). To solve the synonymity problem and the data sparseness problem, we give a mapping strategy which maps pathogenesis to standardized forms and maps drugs to drug categories. With the mapping strategy the number of frequent drug sets rises from 287 to 1184. The experimental results indicate that our method with the mapping strategy is an effective way to acquire valuable semantic association rules. © 2012 IEEE.

关键词： Association rules

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A Novel Neuron in Kernel Domain

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International Scholarly Research Notices 2013年第1期2013卷

作者： Zahra Khandan Hadi Sadoghi Yazdi Department of Computer Science Ferdowsi University of Mashhad Mashhad Iranum.ac.ir Center of Excellence on Soft Computing and Intelligent Information Processing Ferdowsi University of Mashhad Iranum.ac.ir

Kernel-based neural network (KNN) is proposed as a neuron that is applicable in online learning with adaptive parameters. This neuron with adaptive kernel parameter can classify data accurately instead of using a multilayer error backpropagation neural network. The proposed method, whose heart is kernel least-mean-square, can reduce memory requirement with sparsification technique, and the kernel can adaptively spread. Our experiments will reveal that this method is much faster and more accurate than previous online learning algorithms.

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An efficient two-stage level set segmentation framework for overlapping plant leaf image

An efficient two-stage level set segmentation framework for ...

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8th International Conference on intelligent computing Technology, ICIC 2012

作者： Wang, Xiao-Feng Min, Hai Key Lab. of Network and Intelligent Information Processing Department of Computer Science and Technology Hefei University Hefei Anhui 230022 China Intelligent Computing Lab. Hefei Institute of Intelligent Machines Chinese Academy of Sciences P.O.Box 1130 Hefei Anhui 230031 China Department of Automation University of Science and Technology of China Hefei Anhui 230027 China

ISBN: (纸本)9783642315879

In this paper, an efficient two-stage segmentation framework was proposed to address the plant leaf image with overlapping phenomenon, which is built based on the leaf approximate symmetry and level set evolution theory. In the pre-segmentation stage, a straight line was manually set on the target leaf to approximate the principal leaf vein and the Local Chan-Vese (LCV) model was used on the global image region to help searching the so-called un-overlapping contour in target leaf. In the formal segmentation stage, the symmetry detection was performed based on the pre-defined approximated principal vein to obtain the narrow-band evolution region and the second initial contour. Next, the LCV model was once again used to find the complete target leaf contour in the narrow-band evolution region. Finally, experiments on some real leaf images with overlapping phenomenon have demonstrated the efficiency and robustness of the proposed segmentation framework. © 2012 Springer-Verlag.

关键词： Image segmentation

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Left-to-right tree-to-string decoding with prediction

Left-to-right tree-to-string decoding with prediction

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2012 Joint Conference on Empirical Methods in Natural Language processing and Computational Natural Language Learning, EMNLP-CoNLL 2012

作者： Feng, Yang Liu, Yang Liu, Qun Cohn, Trevor Department of Computer Science University of Sheffield Sheffield United Kingdom State Key Laboratory on Intelligent Technology and Systems Department of Computer Sci. and Tech. Tsinghua University Beijing China Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences Beijing China

ISBN: (纸本)9781937284435

Decoding algorithms for syntax based machine translation suffer from high computational complexity, a consequence of intersecting a language model with a context free grammar. Left-to-right decoding, which generates the target string in order, can improve decoding efficiency by simplifying the language model evaluation. This paper presents a novel left to right decoding algorithm for tree-to-string translation, using a bottom-up parsing strategy and dynamic future cost estimation for each partial translation. Our method outperforms previously published tree-to-string decoders, including a competing left-to-right method. © 2012 Association for Computational Linguistics.

关键词： Context free grammars

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Leaf image recognition using Fourier transform based on ordered sequence

Leaf image recognition using Fourier transform based on orde...

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8th International Conference on intelligent computing Technology, ICIC 2012

作者： Yang, Li-Wei Wang, Xiao-Feng Department of Automation University of Science and Technology of China Hefei Anhui 230027 China Intelligent Computing Laboratory Hefei Institute of Intelligent Machines Chinese Academy of Sciences P.O. Box 1130 Hefei Anhui 230031 China Key Lab. of Network and Intelligent Information Processing Hefei University Hefei Anhui 230601 China

ISBN: (纸本)9783642315879

There are a number of leaf recognition methods, but most of them are based on Euclidean space. In this paper, we will introduce a new description of feature for the leaf image recognition, which represents the leaf contour with the ordered sequence. For a leaf image, points on the contour represent the most important information of the leaf. Thus, by extracting serial points of the leaf contour, the unique corresponding ordered sequence can be obtained for a contour. Then, we can compute the amplitude-frequency feature by performing the Discrete Fourier transform on the ordered sequence. Since the low-frequency part of the Fourier transform represents the global information and the high-frequency part the local details, we can adopt the amplitude-frequency feature for leaf image recognition. Experimental results on the famous Swedish library and ICL library show that the proposed feature is effective for leaf image recognition. © 2012 Springer-Verlag.

关键词： Image recognition

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Memory performance prediction of web server applications based on grey system theory

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14th Asia Pacific Web Technology Conference, APWeb 2012

作者： Huang, Faliang Zhang, Shichao Yuan, Changan Zhong, Zhi Faculty of Software Fujian Normal University Fuzhou 350007 China Faculty of Engineering and Information Technology University of Technology Broadway P.O. Box 123 Sydney NSW 2007 Australia Science Computing and Intelligent Information Processing GuangXi Higher Education Key Laboratory Nanning 530023 China

ISBN: (纸本)9783642292521

With the success of internet, recently more and more companies start to run web-based business. While running e-business sites, many companies have encountered unexpected degeneration of their web server applications performance, which may lead to loss of customers. Many managers wish to have a decision-support tool that cancan answer such questions, such as "will my web server applications performance degenerate?", and "what are the main reasons of the degenerations?". In this paper we first propose a new memory performance prediction model of web server applications based on grey system theory. And then, a software system "Memory Performance Manager" (MPM) is developed for predicting memory performance of the web server applications. Massive experiments demonstrate that the effectiveness of MPM's in predicting web server memory performances. © 2012 Springer-Verlag Berlin Heidelberg.

关键词： Managers

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