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International Conference on Advanced information Networking and Applications (AINA)

作者： Hai Zhuge Knowledge Grid Research Group Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy and Sciences Beijing China

ISBN: (纸本)9781424466955;9780769540184

Humans have been recognizing, establishing and making use of various relations consciously and unconsciously since the formation of human society. Waving and maintaining various relations accompany everyone's life. Humans' social behaviors create or emphasize relations. Various networks evolve with the development of society. Some relational networks gradually become independent of individuals' life during evolution so that they become a part of knowledge and culture sharable cross generations. The study of social relations trace back to the age of Laozi (576-BC), Confucius (551-479 BC), Thales (624 BC-546 BC) and Socrates (469 BC-399 BC). The formal definition of general relation traces to the invention of set theory in 1874. Humans have intelligence to observe and participate in social processes, to think, and to know the effect of establishing a relation. Humans can also actively select appropriate relations and persons according to requirement, situation and social rules. Machines are obviously limited in these abilities. Various graph-based models have been used to connect resources in the cyber space. Two issues are fundamental: (1) machines know little relation in human society and the nature, data structures in machines are for machines to process not for humans to read, so it is not realistic to expect machines to discover social and natural laws and resolve relevant issues without human instruction; and, (2) machines are hard to know the effect of establishing and making use of relations, and to explain computing result according to society and nature. The cause is that machines do not have any worldview. Connecting various networks and machines with nature, society, and even human minds can create a new world where individuals have semantic images that can enhance mutual understanding. The semantic images can be constructed by a Semantic Link Network SLN consisting of nodes with rich semantics, semantic links between nodes, and rules for reasoning, influe

关键词： Humans Societies Libraries Hypertext systems Spatial databases intelligent networks information processing Computers Set theory Machine intelligence

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Using genetic algorithms to optimize artificial neural networks

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Journal of Convergence information technology 2010年第8期5卷 6-6页

作者： Ding, Shifei Xu, Li Su, Chunyang Zhu, Hong 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 100080 China

Artificial Neural Networks (ANNs), as a nonlinear and adaptive information processing systems, play an important role in machine learning, artificial intelligence, and data mining. But the performance of ANNs is sensitive to the number of neurons, and chieving a better network performance and simplifying the network topology are two competing objectives. While Genetic Algorithms (GAs) is a kind of random search algorithm which simulates the nature selection and evolution, which has the advantages of good global search abilities and learning the approximate optimal solution without the gradient information of the error functions. This paper makes a brief survey on ANNs optimization with GAs. Firstly, the basic principles of ANNs and GAs are introduced, by analyzing the advantages and disadvantages of GAs and ANNs, the superiority of using GAs to optimize ANNs is expressed. Secondly, we make a brief survey on the basic theories and algorithms of optimizing the network weights, optimizing the network architecture and optimizing the learning rules, and make a discussion on the latest research progresses. At last, we make a prospect on the development trend of the theory.

关键词： Genetic algorithms

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A novel ant colony genetic hybrid algorithm

Journal of Software

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Journal of Software 2010年第11期5卷 1179-1186页

作者： Gao, Shang Zhang, Zaiyue Cao, Cungen School of Computer Science and Technology Jiangsu University of Science and Technology Zhenjiang 212003 China Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences Beijing 100080 China

By use of the properties of ant colony algorithm and genetic algorithm, a novel ant colony genetic hybrid algorithm, whose framework of hybrid algorithm is genetic algorithm, is proposed to solve the traveling salesman problems. The selection operator is an artificial version of natural selection, and chromosomes with better length of tour have higher probabilities of being selected in the next generation. Based on the properties of pheromone in ant colony algorithm the ant colony crossover operation is given. Four mutation strategies are put forward using the characteristic of traveling salesman problems. The hybrid algorithm with 2-opt local search can effectively find better minimum beyond premature convergence. Ants choose several tours based on trail, and these tours will replace the worse solution. Compare with the simulated annealing algorithm, the standard genetic algorithm and the standard ant colony algorithm, all the 4 hybrid algorithms are proved effective. Especially the hybrid algorithm with strategy D is a simple and effective better algorithm than others. © 2010 ACADEMY PUBLISHER.

关键词： Genetic algorithms

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An improved bp neural network algorithm based on factor analysis

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Journal of Convergence information technology 2010年第4期5卷

作者： Ding, Shifei Jia, Weikuan Su, Chunyang Liu, Xiaoliang Chen, Jinrong 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 100080 China

Back-Propagation (BP) neural network, as one of the most mature and most widespread algorithms, has the ability of large scale computing and has unique advantages when dealing with nonlinear high dimensional data. But when we manipulate high dimensional data with BP neural network, many feature variables provide enough information, but too many network inputs go against designing of the hidden-layer of the network and take up plenty of storage space as well as computing time, and in the process interfere the convergence of the training network, even influence the the accuracy of recognition finally. Factor analysis (FA) is a multivariate analysis method which transforms many feature variables into few synthetic variables. Aiming at the characteristics that the samples processed have more feature variables, combining with the structure feature of BP neural network, a FA-BP neural network algorithm is proposed. Firstly we reduce the dimensionality of the feature factor using FA, and then regard the features reduced as the input of the BP neural network, carry on network training and simulation with low dimensional data that we get. This algorithm here can simplify the network structure, improve the velocity of convergence, and save the running time. Then we apply the new algorithm in the field of pest prediction to emulate. The results show that under the prediction precision is not reduced, the error of the prediction value is reduced by using the new algorithm, and therefore the algorithm is effective.

关键词： Neural networks

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A parallel Hyper-Surface Classifier for high dimensional data

A parallel Hyper-Surface Classifier for high dimensional dat...

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International Symposium on Knowledge Acquisition and Modeling, KAM

作者： Qing He Qun Wang Chang-ying Du Xu-dong Ma Zhong-zhi Shi Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy and Sciences Beijing China Chinese Academy of Sciences Beijing China

The enlarging volumes of data resources produced in real world makes classification of very large scale data a challenging task. Therefore, parallel process of very large high dimensional data is very important. Hyper-Surface Classification (HSC) is approved to be an effective and efficient classification algorithm to handle two and three dimensional data. Though HSC can be extended to deal with high dimensional data with dimension reduction or ensemble techniques, it is not trivial to tackle high dimensional data directly. Inspired by the decision tree idea, an improvement of HSC is proposed to deal with high dimensional data directly in this work. Furthermore, we parallelize the improved HSC algorithm (PHSC) to handle large scale high dimensional data based on MapReduce framework, which is a current and powerful parallel programming technique used in many fields. Experimental results show that the parallel improved HSC algorithm not only can directly deal with high dimensional data, but also can handle large scale data set. Furthermore, the evaluation criterions of scaleup, speedup and sizeup validate its efficiency.

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An Efficient Data Mining Framework on Hadoop using Java Persistence API

An Efficient Data Mining Framework on Hadoop using Java Pers...

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International Conference on Computer and information technology (CIT)

作者： Yang Lai Shi ZhongZhi Chinese Academy and Sciences Beijing China Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy and Sciences Beijing China

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. A feasible distributed data indexing algorithm is proposed for Hadoop data mining, based on ZSCORE binning and inverted indexing and on the Hadoop SequenceFile format. A data mining framework on Hadoop using the Java Persistence API (JPA) and MySQL Cluster is proposed. The framework is elaborated in the implementation of a decision tree algorithm on Hadoop. We compare the data index-ing algorithm with Hadoop MapFile indexing, which performs a binary search, in a modest cloud environment. The results show the algorithm is more efficient than naïve MapFile indexing. We compare the JDBC and JPA implementations of the data mining framework. The performance shows the framework is efficient for data mining on Hadoop.

关键词： Data mining Indexing Distributed databases Pediatrics Clustering algorithms Decision trees

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IEEE WIC ACM International Conference on Web Intelligence (WI)

作者： Xu Han Zhongzhi Shi Wenjia Niu Kunrong Chen Xinghua Yang Chinese Academy of Sciences Beijing China The Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy and Sciences Beijing China

With the rapid development of XML language which has good flexibility and interoperability, more and more log files of software running information are represented in XML format, especially for Web services. Fault diagnosis by analyzing semi-structured and XML like log files is becoming an important issue in this area. For most related learning methods, there is a basic assumption that training data should be in identical structure, which does not hold in many situations in practice. In order to learn from training data in different structures, we propose a similarity-based Bayesian learning approach for fault diagnosis in this paper. Our method is to first estimate similarity degrees of structural elements from different log files. Then the basic structure of combined Bayesian network (CBN) is constructed, and the similarity-based learning algorithm is used to compute probabilities in CBN. Finally, test log data can be classified into possible fault categories based on the generated CBN. Experimental results show our approach outperforms other learning approaches on those training datasets which have different structures.

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Effective camera motion analysis approach

Effective camera motion analysis approach

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IEEE International Conference on Networking, Sensing and Control

作者： Shugao Ma Weiqiang Wang Chinese Academy and Sciences Beijing China Key Laboratory of Intelligent Information Processing the Institute of Computer Technology Chinese Academy and Sciences Beijing China

ISBN: (纸本)9781424464500;9781424464531

Camera motion analysis (CMA) is very useful for many video content analysis tasks, but few works competently handle the videos with significant camera or object motion. In this paper, we present an effective CMA approach for such challenging cases. The effectiveness of our approach comes from two aspects: (1) reliably matching keypoints on consecutively sampled frames, where both the appearance similarity and motion smoothness of keypoints are considered; (2) effectively distinguishing background keypoints from foreground ones by a novel and advanced voting process, where the voting weights of keypoints are dynamically adjusted to guarantee that the influence of foreground motion can be largely reduced in CMA. Thus, a parametric camera motion model can be naturally derived by the accurate estimation of the motion of background keypoints. The experimental results on the TRECV2005 dataset and 500 shots from seven classic action movies demonstrate the effectiveness of our approach.

关键词： Cameras Motion analysis Motion estimation Image motion analysis Pattern analysis Voting information analysis Event detection Optical filters Vectors

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Voting conditional random fields for multi-label image classification

Voting conditional random fields for multi-label image class...

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Congress on Image and Signal processing, CISP

作者： Xishun Wang Xi Liu Zhiping Shi Zhongzhi Shi Hongjian Sui Chinese Academy of Sciences Beijing China The Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy and Sciences Beijing China

In our real world, there usually exist several different objects in one image, which brings intractable challenges to the traditional pattern recognition methods to classify the images. In this paper, we introduce a Conditional Random Fields (CRFs) model to deal with the Multi-label Image Classification problem. Considering the correlations of the objects, a second-order CRFs is constructed to capture the semantic associations between labels. Different initial feature weights are set to introduce the voting techniques for a better performance. We evaluate our methods on MSRC dataset and demonstrate high precision, recall and F 1 measure, showing that our method is competitive.

关键词： Visualization Training Image classification Correlation Buildings Image segmentation Semantics

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A New Roadmap Constitution Method Based on Dividing Grid and Level-Set

A New Roadmap Constitution Method Based on Dividing Grid and...

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International Symposium on Intelligence information processing and Trusted Computing (IPTC)

作者： Shidong Li Mingyue Ding Chao Cai Institute for Information Engineering Hubei University Hubei China College of Life Science and Technology Image Processing and Intelligent Control Key Laboratory of HUST Wuhan China National Laboratory for Multi-spectral Information Processing Technologies Wuhan China

Roadmap methods were widely used in route planning fields, both for robots and unmanned aircrafts. Traditional roadmap is constituted by connecting the vertexes of convex obstacle, which is related to the locations of start and goal of route, so the roadmap can not be reused when anyone of start and goal changed. Absence of reusability reduces the efficiency of route planning by using roadmap. A new roadmap constitution method was proposed in this paper, which is build on dividing the 2D configuration space into multi-scale grids and setting nodes of roadmap on grid boundary using Level-Set method, same method was used to construct the map cells of roadmap between any two nodes lays on same grid. The roadmap constituted with new method can be reused when the start or goal was changed; it is efficient for replanning problem.

关键词： Planning Level set Constitution Path planning Equations Robots information processing

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