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Kernel function clustering algorithm with optimized parameters

Kernel function clustering algorithm with optimized paramete...

引用

international conference on machine learning and Cybernetics (icmlc)

作者： Jiu-Zhen Liang Jiang-Hua Gao School of Information Science & Engineering Zhejiang Normal University ZJNU Jinhua China Math & Physics Department Dalian Jiaotong University Dalian China

This paper deals with kernel function clustering algorithm with optimized parameter. Traditional clustering problems and solving algorithms are analyzed, and several limitations of traditional clustering algorithm are listed. These limitations are overcome by introducing kernel functions, which a nonlinear problem is transformed into a high dimension space. This paper proposes a kind of kernel function clustering algorithm with parameters optimized. Using these techniques, the nonlinear clustering problem in the high dimension space become simpler in which the inner distances of sample in the same class are shrunk and the distances between two class centers are increased relatively. The algorithm computing complexity is analyzed and a strategy of reducing complexity is presented. Also the primary factor of affecting clustering precision is discussed through an experiment example.

关键词： Kernel Clustering algorithms Algorithm design and analysis Scattering Shape Information science Pattern recognition Statistics Employment machine learning

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machine learning for automatic acquisition of Chinese linguistic ontology knowledge

Machine learning for automatic acquisition of Chinese lingui...

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international conference on machine learning and Cybernetics (icmlc)

作者： De-Quan Zheng Tie-Jun Zhao Feng Yu Sheng-Li MOE-MS Key Laboratory of Natural Language Processing and Speech Harbin Institute of Technology Harbin China School of Computer Science and Engineering Harbin University of Commerce Harbin China

Due to the complexity and flexibility of natural language, automatic linguistic knowledge acquisition and its application research becomes difficult. In this paper, we present a machine learning method to automatically acquire Chinese linguistic ontology knowledge from typical corpus. This study, first, defined the description frame of Chinese linguistic ontology knowledge, and then, automatically acquired the usage of a Chinese word with its co-occurrence of context in using semantic, pragmatics, syntactic, etc from the corpus, final, the above information and their representation act as Chinese linguistic ontology knowledge bank. We completed two groups of experiments, i.e. documents similarity computing, text reordering for information retrieval. Compared with previous works, the proposed method solves the inferior precision of nature language processing.

关键词： machine learning Ontologies Natural languages Natural language processing Knowledge acquisition Tagging learning systems Information retrieval Computer science Knowledge engineering

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learning of weighted fuzzy production rules based on fuzzy neural network

Learning of weighted fuzzy production rules based on fuzzy n...

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international conference on machine learning and Cybernetics (icmlc)

作者： Dong-Mei Huang Ming-Hu Ha Xue-Fei Li Tsang Ya-Min Li College of Science Agricultural University of Hebei Baoding China Faculty of Mathematics and Computer Science Hebei University Baoding China Department of Computing Hong Kong Polytechnic University Kowloon Hong Kong China Artificial Intelligence Research Center Agricultural University of Hebei Baoding China

In this paper, we develop a fuzzy neural network (FNN) with a new BP learning algorithm using some smooth function, which is used to refine or tune the local and global weights of fuzzy production rules (FPRs) so as to enhance the representation power of FPRs by including local and global weights. By experimenting our method with some existing benchmark examples, the proposed method is found have high accuracy in classifying unseen samples without increasing the number of the extracted FPRs, and furthermore, the time required to consult with domain experts for gaining a rule is greatly reduced.

关键词： Fuzzy neural networks Production Fuzzy systems Refining Training data Induction generators Agriculture Fuzzy reasoning Hybrid intelligent systems Data mining

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Document indexing in text categorization

Document indexing in text categorization

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international conference on machine learning and Cybernetics (icmlc)

作者： Qi-Rui Zhang Ling Zhang Shou-Bin Dong Jing-Hua Tan Guangdong Key Laboratory of Computer Network South China University of Technology Guangzhou China China Telecom-Guangzhou Research and Development Center Guangzhou China

Aiming at the characteristic of text categorization, this paper proposes an improved method of computing term weights, tfidfie, based on the traditional tfidf function that is generally used in most classifiers. In comparison with the tfidf function, the tfidfie function adds an information entropy factor, H, which represents the distribution of documents in the training set in which the term occurs. The experiments show tfidfie outperforms tfidf. In addition, this paper analyses the difference of using information entropy factor H between document categorization and feature selection, also finds that both two phases are all necessary for text categorization, meanwhile it can reach the best performance of classification with up to 70% of the unique terms being removed.

关键词： Indexing Text categorization Information retrieval machine learning Intelligent networks Computer networks Information entropy Classification tree analysis Frequency Laboratories

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Effectively extracting rules from trained neural networks based on the characteristics of the classification hypersurfaces

Effectively extracting rules from trained neural networks ba...

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international conference on machine learning and Cybernetics (icmlc)

作者： De-xian Zhang Yang Liu Zi-qiang Wang School of Information Science and Engineering Henan University of Technology Zhengzhou China Computer College Northwestern Polytechnical University Xi'an China

The lack of efficient heuristic information is the fundamental reason that causes the low effectiveness of currently used approaches for extracting symbolic rules from trained feedforward neural networks. In this paper, a new measurement method of the classification power of attributes on the basis of the characteristics of the classification hypersurfaces is proposed, which is suitable for both continuous attributes and discrete attributes. Based on this new measurement method, a new approach for rule extraction from trained neural networks and classification problems with continuous attributes is proposed. A new method to adjust the complexity of neural network models based on the output range adjustment is also presented. The performance of the new approach is demonstrated by several computing cases. The results of the experiments prove that the approach proposed can improve the validity of the extracted rules remarkably compared with other rule extracting approaches, especially for the complicated classification problems.

关键词： Neural networks Data mining Shape Classification tree analysis Decision trees Information science Computer networks Power engineering computing Educational institutions Electronic mail

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Decision rule extraction method based on rough set theory and fuzzy set theory

Decision rule extraction method based on rough set theory an...

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international conference on machine learning and Cybernetics (icmlc)

作者： Ming-Chun Wang Zheng-Ou Wang Ming Zhang Peng Yan Mathematics and Physics Department Tianjin university of Education and Technology Tianjin China Institute of Systems Engineering Tianjin university Tianjin China Faculty of Mathematics and Computer Science Hebei University Baoding China Mathematics and Physics Department Tianjin University of Technology and Education Tianjin China

A quantitative decision table can be transformed into a qualitative decision one by using the fuzzy set theory. This paper develops the definition of membership function mentioned in the literature, and proposes transforming rules from the quantitative decision table to the qualitative decision table with the properties of membership function. The rules can change an n-dimension quantitative decision table into an n-dimension qualitative decision table instead of a 3n-dimension one. So it greatly decreases afterward computing complexity of rule extraction using rough set theory, while increases the quality of extracted rules.

关键词： Set theory Fuzzy set theory Computer science education Hip Medical diagnosis machine learning Cybernetics Tellurium

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Continuous-attributes reduction in incomplete information system based on rough sets technique

Continuous-attributes reduction in incomplete information sy...

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international conference on machine learning and Cybernetics (icmlc)

作者： Tsang Su-Yun Zhao Yeung Lee Department of Computing Hong Kong Polytechnic University Kowloon Hong Kong China Machine Learning Center Faculty of Mathematics and Computer Science Hebei University Baoding China

Many methods based on rough sets to deal with incomplete information system have been proposed in recent years. However, they are only suitable for the nominal datasets. So far only a few methods based on rough sets to deal with incomplete information system with continuous-attributes have been proposed. In this paper we propose one generalized model of rough sets to reduce continuous-attributes in an incomplete information system. The definition of a relative discernible measure is firstly proposed, which is the underlying concept to redefine the concepts of knowledge reduction such as the reduct and core. The advantage of the proposed method is that instead of preprocessing continuous data by discretization or fuzzification, we can reduce an incomplete information system directly based on the generalized model of rough sets. Finally, a numerical example is given to show the feasibility of our proposed method.

关键词： Information systems Rough sets machine learning Knowledge acquisition Mathematics Computer science Pattern recognition Data mining Process control Set theory

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Fast intra prediction mode decision using parallel processing

Fast intra prediction mode decision using parallel processin...

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international conference on machine learning and Cybernetics (icmlc)

作者： Jamil-Ur-Rehman Ye Zhang Department of Electronics and communication Engineering Harbin Institute of Technology Harbin China

H.264 adapts rate distortion optimization (RDO) technique to get high coding rate and visual quality. Because RDO is too much exhaustive, several attempts have been made to explore fast algorithms. In all algorithms computational speed increased with a performance degradation. In the previous attempts only two variables were considered i.e. computational speed (CS) and performance (P). In this paper we also considered a third variable production cost (PC). In microelectronics devices, FPGA or ASIC, parallel processing enhances computation speed with the increase in production cost. In some cases when no compromise on performance, parallel processing is an effective tool to overcome computational complexity.

关键词： Parallel processing Production Costs Rate-distortion Degradation Microelectronics Field programmable gate arrays Application specific integrated circuits Concurrent computing Computational complexity

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Described Model of a Modular Self-Reconfigurable Robot

Described Model of a Modular Self-Reconfigurable Robot

引用

international conference on machine learning and Cybernetics (icmlc)

作者： Qiu-Xuan Wu Guang-Yi Cao Yan-Qiong Fei Research Institute of Fuelcell Shanghai Jiaotong University Shanghai China Research Institute of Robot Shanghai Jiaotong University Shanghai China

A homogeneous lattice modular Self-Reconfigurable (MSR) robot was designed in the paper, we discuss how to describe configuration of robot using graphs theory, how to discover a robot configuration, utilizing connecting status of six connection component, a feature vector matrix was proposed in order to accurately described the topology structure, position and connection relation of MSR robot. Basic movement and meta-module structure was introduced too. A locomotion example of robot was demonstrated, it verify the correctness of MSR robot model described. The methodology establishes foundation to further study self-reconfiguration algorithm, it is very general and can be applied easily to other modular robots.

关键词： Orbital robotics Lattices Intelligent robots Military computing Crystallization Shape Graph theory Joining processes Topology Mechatronics

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A kind of dimension reduction method for classification based on hyper surface

A kind of dimension reduction method for classification base...

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international conference on machine learning and Cybernetics (icmlc)

作者： Qing He Xiu-rong Zhao Zong-Zhi Shi The Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy and Sciences Beijing China

Based on Jordan curve theorem, a universal classification method based on hyper surface is recently put forward. The experiments show that the new method can efficiently and accurately classify large data size up to 10/sup 7/ in three-dimensional space. However, the number of training samples needed to design a classifier grows with the dimension of the features. So a way to reduce the dimension of the features without losing any essential information is needed. We put forward a kind of simple and efficient dimension reduction method without losing any essential information to improve the performance of classification based on hyper surface for high dimension data.

关键词： Data mining Pattern recognition Data analysis Feature extraction Helium Laboratories Information processing Computers Electronic mail Space technology

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