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International Conference on Fuzzy Systems and Knowledge Discovery (FSKD)

作者： Yi Dong Wei Cheng Shengfeng Li Institute of Applied Mathematics Bengbu College China Key Lab. of Intelligent Computing & Signal Processing Anhui University Hefei China

SVM (Support Vector Machines) is a novel algorithm of machine learning which is based on SLT (Statistical Learning Theory). It can solve the problem characterized by nonlinear, high dimension, small sample and local minimizing perfectly. For non-linear problem, the forecasting technique of FCTR (First classification, then regression) was proposed, based on the classification approach of SVM and has carried on the simulation experiment. The experiment shows that the fitting value which obtains using the return to first would be more precise than directly. Using this method to food production forecast, its accuracy is superior to other production forecasting methods.

关键词： Support vector machines Production Predictive models Forecasting Numerical models Educational institutions Integrated circuit modeling

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Robust and discriminative image authentication based on sparse coding

Robust and discriminative image authentication based on spar...

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2011 IEEE Consumer Communications and Networking Conference, CCNC'2011

作者： Mou, Luntian Huang, Tiejun Tian, Yonghong Lian, Shiguo Chen, Xilin Key Laboratory of Intelligent Information Processing Institute of Computing Technology CAS Beijing China Graduate University of Chinese Academy of Sciences Beijing China Institute of Digital Media Peking University Beijing China France Telecom RandD Beijing China

ISBN: (纸本)9781424487905

Image authentication is usually approached by checking the preservation of some invariant features, which are expected to be both robust and discriminative so that content-preserving operations are accepted while content-altering manipulations are rejected. However, most of existing features have not obtained convincing performance due to insufficiency of experiments and over biasing of robustness. Motivated by the sparse coding strategy discovered in primary visual cortex, we explore the possibility of using sparse coding coefficients for image authentication. Through extensive experiments, we discover that the proposed feature bears great discrimination as well as robustness, which indicates the effectiveness of sparse coding as a new invariant feature for image authentication. © 2011 IEEE.

关键词： Authentication

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A novel description of the reproducing kernel support vector machines

A novel description of the reproducing kernel support vector...

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IEEE International Conference on Computer Science and Automation Engineering (CSAE)

作者： Li-xiang Xu Bin Luo Feng-hai Yu Jin Xie Department of Mathematics & Physics Hefei University Hefei China Key Laboratory of Intelligent Computing & Signal Processing of Ministry of Education School of Computer Science and Technology Anhui University Anhui China

Support vector machines (SVMs) and related kernel-based algorithms have become one of the most popular approaches for many machine learning problems. but little is known about the structure of their reproducing kernel Hilbert spaces (RKHS). In this work, based on Mercer's Theorem, the relation among reproducing kernel (RK) and Mercer kernel, and their roles in SVMs are discussed, corresponding to some important theorems and consequences are given. Furthermore, a novel framework of reproducing kernel support vector machines (RKSVM) is proposed. The simulation results are presented to illustrate the feasibility of the proposed method. Choosing a proper Mercer kernel for different tasks is an important factor for studying the result of the SVMs.

关键词： Kernel Support vector machines Hilbert space Equations Mathematical model Resonant frequency Simulation

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Monitoring the PSO Process Based on Fuzzy Random Variables

Monitoring the PSO Process Based on Fuzzy Random Variables

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International Conference on Computational Intelligence and Security

作者： J.Q. Feng C. Xu W.X. Xie Institute of Intelligent Computing Science Shenzhen University Shenzhen China Key Laboratory of Intelligent Information Processing Shenzhen University Shenzhen Guangdong China

This paper presents a method for monitoring the particle swarm optimization process that accounts for the random nature of the system's external environment and the fuzzy character of the particles' decision-making process by regarding the fitness function as a fuzzy random variable. The belief level value and the Borel set of chance measures are also used to monitor the particle swarm optimization process and two simulation experiments show the congregate scenes of the particle swarm optimization.

关键词： Particle swarm optimization Random variables Convergence Monitoring Fuzzy sets Algorithm design and analysis Optimization

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A uniform solution to common algorithmic problem by tissue p systems with cell division

A uniform solution to common algorithmic problem by tissue p...

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6th International Conference on Bio-Inspired computing: Theories and Applications, BIC-TA 2011

作者： Niu, Yunyun Pan, Linqiang Pérez-Jiménez, Mario J. Key Laboratory on Image Processing and Intelligent Control Department of Control Science and Engineering Huazhong University of Science and Technology Wuhan 430074 Hubei China Research Group on Natural Computing Department of Computer Science and Artificial Intelligence University of Sevilla Sevilla 41012 Spain

ISBN: (纸本)9780769545141

Common algorithmic problem is an optimization problem, which has the nice property that several other NPcomplete problems can be reduced to it in linear time. A tissue P system with cell division is a computing model which has two basic characters: intercellular communication and the ability of cell division. The ability of cell division allows us to obtain an exponential amount of cells in linear time and to design cellular solutions to computationally hard problems in polynomial time. We here present an effective solution to the common algorithmic decision problem using a family of recognizer tissue P systems with cell division. © 2011 IEEE.

关键词： Polynomial approximation

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MST based stream data clustering algorithm

International Journal of Digital Content Technology and its ...

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International Journal of Digital Content Technology and its Applications 2011年第9期5卷 352-360页

作者： Cheng, Zheng Xiaodong, Zhu Jingcheng, Li Key Laboratory of Intelligent Computing and Signal Processing of the Ministry of Education Anhui University China School of Computer Science and Technology Anhui University China Institute of Information Management and Electronic Business University of Shanghai for Science and Technology China

How to make use of limited memory space and processing speeds of computer for rapid and accurate data mining has become an important research topic on the stream data cluster analysis. A stream data clustering algorithm based on the minimum spanning tree (MSTSC) is described. MSTSC is divided into online processing and offline clustering. Stream data are analyzed online by using two groups of processing unit respectively. In offline process clusters is taken as representative objects, and the minimum spanning tree algorithm is used in offline clustering. MSTSC can improve the clustering quality on non-spherical clusters. Some experiments are carried out in both real data sets and synthetic data sets. Results show that MSTSC algorithm not only can deal with non-spherical clusters effectively, but also has better efficiency and clustering quality. In addition, MSTSC is insensitive to order of input data, and has a good effect for skewed class distributions.

关键词： Cluster analysis

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An intelligent load balancing algorithm towards efficient cloud computing

An intelligent load balancing algorithm towards efficient cl...

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2011 AAAI Workshop

作者： Xu, Yang Wu, Lei Guo, Liying Chen, Zheng Yang, Lai Shi, Zhongzhi Agent-based Cooperative System Lab School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu SC 611731 China Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences Beijing 100190 China

ISBN: (纸本)9781577355243

MapReduce provided a novel computing model for complex job decomposition and sub-tasks management to support cloud computing with large distributed data sets. However, its performance is significantly influenced by the working data distributions over those data sets. In this paper, we put forward a novel model to balance data distribution to improve cloud computing performance in data-intensive applications, such as distributed data mining. By extending the classic MapReduce model with an agent-aid layer and abstracting working load requests for data blocks as tokens, the agents can reason from previously received tokens about where to send other tokens in order to balance the working tasks and improve system performance. Our key contribution lies in building an efficient token routing algorithm in spite of agents' unknowing to the global state of data distribution in cloud. We also built a prototype of our system, and the experimental results show that our approach can significantly improve the efficiency of cloud computing. Copyright © 2011, Association for the Advancement of Artificial Intelligence. All rights reserved.

关键词： MapReduce

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Dynamic description logics based semantic web services composition

Dynamic description logics based semantic web services compo...

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3rd International Conference on Artificial Intelligence and Computational Intelligence, AICI 2011

作者： Peng, Hui Chen, Limin Zeng, Weizhong Tang, Junjian Education Technology Center Beijing International Studies University Beijing 100024 China Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences Beijing 100190 China Information Engineering School Nanchang University Nanchang 330031 China

ISBN: (纸本)9783642238864

The automatic Web services composition has been a research focus since an ever-increasing numbers of Web services are created and published. In this paper, we present a dynamic description logics (DDLs) based method for automatic Web services composition. DDLs are an extension of description logic with a dynamic dimension. It adds actions expressions into description logic and makes up a set of reasoning mechanism for action expressions. The DDLs reasoner has been built for DDLs formulas. As an Web service can be regarded as an action in Web, its accomplishment changes the state of Web, from original state to aim state, an Web service can be described as an action in DDLs, which this action can be described in terms of formulas in DDLs. Due to these relations between Web service and DDLs actions, our method describes both the goal service and atomic services in terms of DDLs formulas. We proved that if a goal formula can not be satisfied then the services composition can be found. The procedure of checking the satisfiability of the formula is also the procedure of discovering the services composition. Thus the problem of automatic services composition is reduced to the problem of checking the satisfiability of formulas in DDLs. An example of automatic services composition in travel area shows the effectiveness of our method. © 2011 Springer-Verlag.

关键词： Web services

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Fast intra mode selection for stereo video coding using epipolar constraint

Fast intra mode selection for stereo video coding using epip...

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IEEE Visual Communications and Image processing (VCIP)

作者： Guolei Yang Luhong Liang Wen Gao Peking University Beijing China Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences China

Application of stereo video in TV industry and consumer electronics becomes very popular recently. Thus, fast algorithm for stereo video coding is highly desired because of its huge inter-view computational redundancy. In our previous work we proposed an epipolar constraint based fast inter mode selection for stereo video using motion vector of blocks as a indicator of similarity, nevertheless, intra mode selection is also highly complex in standards like H.264/AVC. In this paper, a practical fast intra mode selection is proposed to eliminate the computational redundancy by exploiting inter-view dependency based on epipolar constraint. The proposed method does not rely on disparity estimation. Instead, a sliding window is employed to generate an intra mode candidate pool from macro-blocks on the epipolar line. The candidate pool is then rectified to remove invalid modes and improve accuracy. Finally, optimal prediction mode is selected from the candidate pool. The proposed method can significantly reduce the number of mode candidates/prediction directions compared to exhaustive mode selection by 79%. Experiments on 5 HD video coded in 1-frame demonstrate the overall coding time of one view is saved by 56% on average, with slightly video quality loss less than 0.1 dB.

关键词： Encoding Prediction algorithms Redundancy signal processing algorithms Video coding Estimation Complexity theory

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Parallel Outlier Detection Using KD-Tree Based on MapReduce

Parallel Outlier Detection Using KD-Tree Based on MapReduce

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IEEE International Conference on Cloud computing Technology and Science (CloudCom)

作者： Qing He Yunlong Ma Qun Wang Fuzhen Zhuang Zhongzhi Shi The Key Laboratory of Intelligent Information Processing Institute of Computing Technology CAS Beijing China Chinese Academy of Sciences Beijing China

Distributed and Parallel algorithms have attracted a vast amount of interest and research in recent decades, to handle large-scale data set in real-world applications. In this paper, we focus on a parallel implementation of KD-Tree based outlier detection method to deal with large-scale data set. As one of the state-of-the-art outlier detection methods, KD-Tree based has been approved to be an effective algorithm. However, it still cannot process large-scale data set efficiently due to its serial implementation. Based on the current and powerful parallel programming framework -- MapReduce, we propose to implement the parallel KD-Tree based outlier detection algorithm (e.g., PKDTree for short). Experimental results demonstrate the efficiency of PKDTree according to the evaluation criterions of scale up, speedup and size up.

关键词： Smoothing methods Parallel algorithms Computers Arrays Detection algorithms Algorithm design and analysis Runtime

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