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Congress on Evolutionary Computation

作者： Ruochen Liu Licheng Jiao Wei Zhang Jingjing Ma Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education of China Institute of Intelligent Information Processing Xidian University Xi'an China

Vector Quantization (VQ) is a useful tool for data compression and can be applied to compress the data vectors in the database. The quality of the recovered data vector depends on a good codebook. Mean/residual vector quantization (M/RVQ) has been shown to be efficient in the encoding time and it only needs a little storage. In this paper, Clonal Selection Algorithm for Image Compression (CSAIC) is proposed. In CSAIC, Based on M/RVQ algorithm, an improved clonal selection algorithm is used to cluster the data of compressed images in order to obtain the optimal codebook. The proposed method has been extensively compared with Linde-Buzo-Gray(LBG), Self-Organizing Mapping (SOM) and Modified K-means(Mod-KM) over a test suit of seven natural images. The experimental results show that CSAIC outperforms other three algorithms in terms of image compression performance.

关键词： Image coding Clustering algorithms Algorithm design and analysis Training Encoding Vector quantization

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Unstructured P2P-enabled service discovery in the cloud environment

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作者： Zhou, Jing Shi, Zhongzhi Communication University of China Beijing 100025 China Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences Beijing 100190 China

ISBN: (纸本)3642163262

As the Cloud computing appears to be part of the mainstream computing in a few years, the number of the services it provides, its users, and the requests of these services will be on the rise accordingly. To deliver satisfactory experience to Cloud users, it is essential that highly scalable techniques for service discovery should be available. We embarked on a preliminary study, in Cloud environments, on service discovery by adopting an unstructured P2P technique. In the context of Cloud computing, we start by examining proposed and deployed solutions to service discovery, discuss the methodology of developping efficient mechanisms for service description, description indexing, and query routing, and finally identify open issues. © 2010 IFIP.

关键词： Cloud computing

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Algorithm of Partition based Network Boosting for imbalanced data classification

Algorithm of Partition based Network Boosting for imbalanced...

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International Joint Conference on Neural Networks (IJCNN)

作者： Gou Shuiping Yang Hui Jiao Licheng Zhuang Xiong Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education of China and Institute of Intelligent Information Processing Xidian University Xi'an 710071 China

Network Boosting (NB) is an ensemble learning method which combines weak learners together based on a network and can learn the target hypothesis asymptotically. NB has higher generalization ability compared to Bagging and AdaBoost. But, when datasets are class-imbalanced, the performance of NB will decrease quickly. In order to solve this problem, we present a Partition based Network Boosting method (PNB) to classify imbalanced data. For PNB method, every classifier node of the classifier network is provided with the same number of training data which are all of same weights. The classifier in the network is built by the balanced training set sampled from the training data according to the weights record of the training data it holds. And then, the weights of the instances of every node classifier are updated based on the classification results of self-node and its neighbor nodes. The classifier network is trained repeatedly in such a way. Weight factor of hypothesis in the training progress is introduced to improve the performance. The final classification is formed by all the hypotheses of the classifier network learned during the training progress so that the label of new instances can be decided by the weight voting. The experimental results on UCI data and imbalanced biomedical data show that the PNB algorithm has better AUC and recall performance compared with NB learning machine.

关键词： Laboratories Impedance matching Breast Boosting Liver

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Research on Ranking Support Vector machine and prospects

Research on Ranking Support Vector machine and prospects

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作者： Ding, Shi-Fei Liu, Xiao-Liang Zhang, Li-Wen School of Computer Science and Technology China University of Mining and Technology Xuzhou 221008 China Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences Beijing 100080 China

ISBN: (纸本)9787894631046

Learning to rank is designed to determine a ranking for the target objects according to some rule. Specifically, the problem about learning to rank is to learn a ranking function from a training set whose data has been ranked. It is most applied to the social sciences and information retrieval. Learning to rank is a hot issue in the field of information retrieval and machine learning at present. This paper analyses the process of Ranking Support Vector machine (RSVM) from a theoretical point of view from the classification and regression respectively, and sets up the two basic mathematical models about RSVM. The general introduction about RSVM in the application, training speed and generalization ability is also given. In the end, we come to a conclusion.

关键词： information retrieval

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Associated clustering and classification method for electric power load forecasting

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作者： Dou, Quansheng Fu, Kailei Zhu, Haiyan Jiang, Ping Shi, Zhongzhi Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences Beijing100080 China School of Computer Science and Technology Shandong Institute of Business and Technology Yantai 26400 China

ISBN: (纸本)3642163262

In the process of power load forecasting, electricity experts always divide the forecasting situation into several categories, and the same category uses the same forecasting model. There exists such a situation that some load curve which domain experts consider belonging to the same category has shown the different characteristics, but some load curve which belongs to different category seems very similar, and usually able to gather into a category by clustering. For this problem, the definition of associated matrix was proposed in this paper, and based on this conception the associated clustering- classification algorithm was proposed, We applied this algorithm to data sample classification for power load prediction, the experiment showed that the classification results obtained by our method were more reliable. © 2010 IFIP.

关键词： Forecasting

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A domain-specific question answering system based on ontology and question templates

A domain-specific question answering system based on ontolog...

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11th ACIS International Conference on Software Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing, SNPD2010

作者： Wang, D.S. Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences China Graduate University of the Chinese Academy of Sciences Beijing China Department of Computer Science Jiangsu University of Science and Technology Zhenjiang China

ISBN: (纸本)9780769540887

The rapid growth in the development of Internet-based information systems increases the demand for natural language interfaces that are easy to set up and maintain. Unfortunately, the problem of deep understanding natural language queries is far from being solved. In this paper, an automated question answering system based on domain ontology and question template is proposed, which does not need deep understanding of user's questions. Ontology was employed to model the interesting domain, and properties of concepts are described by a collection of question templates. The system captures a user's intention by matching his questions to predefined templates, and return answers corresponding to template's query focus. The system has been applied to mobile service consulting area, and experimental results show that the system has high practicability as well as maintainability. © 2010 IEEE.

关键词： Ontology

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A dynamic description logic based system for video event detection

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Frontiers of Electrical and Electronic Engineering in China 2010年第2期5卷 137-142页

作者： Xiaofeng WANG Liang CHANG Zhixin LI Zhongzhi SHI The Key Laboratory of Intelligent Information Processing Institute of Computing TechnologyChinese Academy of SciencesBeijing 100190China Graduate University of Chinese Academy of Sciences Beijing 100049China School of Computer and Control Guilin University of Electronic TechnologyGuilin 541004China

Video event detection is an important research area *** the video event is a key problem in video event *** this paper,we combine dynamic description logic with linear time temporal logic to build a logic system for video event *** proposed logic system is named as LTD_(ALCO)which can represent and inference the static,dynamic and temporal knowledge in one uniform logic *** on the LTD_(ALCO),a framework for video event detection is *** video event detection framework can automatically obtain the logic description of video content with the help of ontology-based computer vision techniques and detect the specified video event based on satisfiability checking on LTD_(ALCO)formulas.

关键词： video event semantics dynamic description logics reasoning ontology

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Extend atomic action definitions of DDL to support occlusions and conditional post-conditions

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作者： Chang, Liang Shi, Zhongzhi Gu, Tianlong School of Computer and Control Guilin University of Electronic Technology Guilin 541004 China Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences Beijing 100080 China

ISBN: (纸本)3642163262

The dynamic description logic DDL provides a kind of action theories based on description logics (DLs). Compared with another important DL-based action formalism constructed by Baader ***., a shortcoming of DDL is the absence of occlusions and conditional post-conditions in the description of atomic actions. In this paper, we extend atomic action definitions of DDL to overcome this limitation. Firstly, we introduce extended atomic action definitions in which the occlusions and conditional post-conditions are incorporated. Secondly, for each atomic action specified by an extended atomic action definition, a function named Expand is introduced to transform it into a choice action which is composed of atomic actions defined by ordinary atomic action definitions. Finally, based on the Expand function, the satisfiability-checking algorithm of DDL is extended to support occlusions and conditional post-conditions. © 2010 IFIP.

关键词： Data description

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Quantum-inspired immune clonal clustering algorithm based on watershed

Quantum-inspired immune clonal clustering algorithm based on...

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Congress on Evolutionary Computation

作者： Yangyang Li Nana Wu Jingjing Ma Licheng Jiao Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education of China Institute of Intelligent Information Processing and State Key Laboratory of Integrated Services Network Xidian University Xi'an China

Based on the concepts and principles of quantum computing, a novel clustering algorithm, called a quantum-inspired immune clonal clustering algorithm based on watershed (QICW), is proposed to deal with the problem of image segmentation. In QICW, antibody is proliferated and divided into a set of subpopulation groups. Antibodies in a subpopulation group are represented by multi-state gene quantum bits. In the antibody's updating, the quantum mutation operator is applied to accelerate convergence. The quantum recombination realizes the information communication between the subpopulation groups so as to avoid premature convergences. In this paper, the segmentation problem is viewed as a combinatorial optimization problem, the original image is partitioned into small blocks by watershed algorithm, and the quantum-inspired immune clonal algorithm is used to search the optimal clustering centre, and make the sequence of maximum affinity function as clustering result, and finally obtain the segmentation result. Experimental results show that the proposed method is effective for texture image and SAR image segmentation, compared with the genetic clustering algorithm based on watershed (W-GAC), and the k-means algorithm based on watershed (W-KM).

关键词： Clustering algorithms Image segmentation Error analysis Radiative recombination Feature extraction Wavelet transforms Partitioning algorithms

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An efficient data indexing approach on Hadoop using Java persistence API

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作者： Lai, Yang Zhongzhi, Shi Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences Beijing 100190 China Graduate University of Chinese Academy of Sciences Beijing 100039 China

ISBN: (纸本)3642163262

Data indexing is common in data mining when working with high-dimensional, large-scale data sets. Hadoop, a cloud computing project using the MapReduce framework in Java, has become of significant interest in distributed data mining. To resolve problems of globalization, random-write and duration in Hadoop, a data indexing approach on Hadoop using the Java Persistence API (JPA) is elaborated in the implementation of a KD-tree algorithm on Hadoop. An improved intersection algorithm for distributed data indexing on Hadoop is proposed, it performs O(M+logN), and is suitable for occasions of multiple intersections. We compare the data indexing algorithm on open dataset and synthetic dataset in a modest cloud environment. The results show the algorithms are feasible in large-scale data mining. © 2010 IFIP.

关键词： Data mining

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