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International Conference on machine learning and Cybernetics (ICMLC)

作者： Chun-Ru Dong Patrick P. K. Chan Wing W. Y. Ng Daniel S. Yeung Key Laboratory of Machine Learning and Computational Intelligence College of Mathematics and Computer Science Hebei University Baoding China Machine Learning and Cybernetics Research Center School of Computer Science and Engineering South China University of Technology Guangzhou China

The radial basis function network (RBFN) has been widely used in various fields such as function regression, pattern recognition, and error detection, etc. However, the structural parameters of RBFN including the number of hidden units, centers vectors, and widths (variances) are one of the most important issues when training a RBFN, which greatly affect the performance of RBFN. So, the objective of this paper is to construct an elementary survey about this problem. Firstly, the fundamental knowledge and notations of RBFN is introduced. Secondly, we summarize most existing network structure initialization methods for RBFN and categorize them into four goups. Then some typical appraoches for each category are introduced and discussed. The disadvantages and virtues for parts of methods are also introduced. Finally, the paper is concluded with a discussion of current difficulties and possible future directions about RBFN architecture selection.

关键词： Training Neurons artificial neural networks Clustering algorithms Optimization machine learning

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Fostering Collaborative Work between educators in higher education

Fostering Collaborative Work between educators in higher edu...

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IEEE International Conference on Systems, Man and Cybernetics

作者： Dulce Mota Carlos Vaz de Carvalho Luis Paulo Reis GILT-Graphics Interaction and Learning Technologies School of Engineering Polytechnic of Porto Porto Portugal FEUP-Faculty of Engineering LIAAC-Artificial Intelligence and Computer Science Laboratory University of Porto Porto Portugal

This paper presents the architecture that supports the collaborative model ACEM (Advanced Collaborative Educational Model) to assist educators in the collaborative design of learning activities, supported by a high-level graphical tool. ACEM embraces the research areas of computer Supported Cooperative Work (CSCW) and learning Design (LD). Some facilities are considered in order to implement the online interactions between educators, namely a shared whiteboard and a conversation room. A workflow descriptive model of the educators' teamwork is also introduced.

关键词： Collaborative work Teamwork Resource management Education computers Computational modeling

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The dynamics of trust in cyberdomains

The dynamics of trust in cyberdomains

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作者： Hoffman, Robert R. Lee, John D. Woods, David D. Shadbolt, Nigel Miller, Janet Bradshaw, Jeffrey M. Institute for Human and Machine Cognition United States Department of Industrial and Systems Engineering University of Wisconsin Madison United States Cognitive Systems Engineering Laboratory Ohio State University United States Department of Artificial Intelligence School for Electronics and Computer Science University of Southampton United Kingdom US Air Force Research Laboratory United States

Some human-centering issues for the Networld based on trust are discussed. People sometimes need help to understand how and when to shift from unjustified trust closer to justified distrust. Trust has aspects of an attitude, an attribution, an expectation, a feeling or belief, an intention, and a trait. Trust can be thought of as a family of relations, certainly not a single relation. Interpersonal trust is both fundamentally and subtly different from trust in automation. Threshold effects are reflected in the tendency to maintain a fixed level of reliance even as the level of trust changes, resulting in a dichotomous pattern of reliance. Cybersecurity and defense issues, including social deception, misdirection, influence, and manipulation, span all the venues of macrocognitive work in the Networld. In macrocognitive work systems, trust can also be thought of as the expectation of reciprocity from others.

关键词： Cognitive systems

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Chinese Text Categorization study based on feature weight learning

Chinese Text Categorization study based on feature weight le...

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International Conference on machine learning and Cybernetics (ICMLC)

作者： Yan Zhan Hao Chen Su-Fang Zhang Mei Zheng Key Laboratory of Machine Learning and Computational Intelligence College of Mathematics and Computer Science Hebei University Baoding China Teaching and research of section of mathematics Hebei Information Engineering School Baoding China College of international education Yanshan University슠 Qinhuangdao China

Text categorization (TC) is an important component in many information organization and information management tasks. Two key issues in TC are feature coding and classifier design. The Euclidean distance is usually chosen as the similarity measure in K-nearest neighbor classification algorithm. All the features of each vector have different functions in describing samples. So we can decide different function of every feature by using feature weight learning. In this paper text categorization via K-nearest neighbor algorithm based on feature weight learning is described. The numerical experiments prove the validity of this learning algorithm.

关键词： Text categorization machine learning Nearest neighbor searches Testing Euclidean distance machine learning algorithms Classification tree analysis Decision trees Support vector machines Support vector machine classification

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FUZZY DECISION TREE BASED ON THE IMPORTANT DEGREE OF FUZZY ATTRIBUTE

FUZZY DECISION TREE BASED ON THE IMPORTANT DEGREE OF FUZZY A...

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2008 International Conference on machine learning and Cybernetics(2008机器学习与控制论国际会议)

作者： XI-ZHAO WANG JUN-HAI ZHAI SU-FANG ZHANG Key Lab.of Machine Learning and Computational Intelligence College of Mathematics and Computer Scie Teaching and research of section of mathematics Hebei Information Engineering School Baoding 0710

There may be many fuzzy attributes in a fuzzy information system. Different fuzzy attribute has different contribution to classification. More important attributes have more contribution than the others to decision-making. In this paper, based on the importance of the fuzzy condition attributes, a new method generating a fuzzy decision tree is proposed, which uses the important degree of the fuzzy condition attribute with respect to the fuzzy decision attributes to select attributes to expand the branches of a fuzzy decision tree. A comparison between the new method and fuzzy ID3 is provided. It is shown that the new method is more efficient than fuzzy ID3.

关键词： Condition attribute Decision attribute Fuzzy attribute Fuzzy decision tree Important degree

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IMAGE RECOGNITION BASED ON WAVELET TRANSFORM AND artificial NEURAL NETWORKS

IMAGE RECOGNITION BASED ON WAVELET TRANSFORM AND ARTIFICIAL ...

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2008 International Conference on machine learning and Cybernetics(2008机器学习与控制论国际会议)

作者： JUN-HAI ZHAI LI-JUAN LIU SU-FANG ZHANG Key Lab.of Machine Learning and Computational Intelligence College of Mathematics and Computer Scie Teaching and research of section of mathematics Hebei Information Engineering School Baoding 07100

This paper proposes an image recognition method, which consists of two steps: features extraction based on wavelet transform and image recognition using artificial neural networks. More specifically, wavelet transform is used to decompose the original image into different frequency sub-bands, then a set of features are extracted from the wavelet coefficients. The feature set as input fed into neural network for recognition. The experimental results confirmed effectiveness of the proposed approach.

关键词： Image recognition Wavelet transform artificial neural networks Multi-scale features

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THE CONCEPT learning IN THE THEORY OF ROUGH SETS

THE CONCEPT LEARNING IN THE THEORY OF ROUGH SETS

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2008 International Conference on machine learning and Cybernetics(2008机器学习与控制论国际会议)

作者： QUN-FENG ZHANG YU-TING JIANG ZHI-QIANG LI Key Lab.of Machine Learning and Computational Intelligence College of Mathematics and Computer Scie Industrial and commercial college Heibei University Baoding 071002 China Hebei Information Engineering School Baoding 071000 China

Knowledge reduction in decision table is important in both theory and application, and it outputs a minimal algorithm as a result. Set of the samples fitting the minimal algorithm is a concept over the set of all possible instances. But in unfamiliar environment, decision table is obtained randomly. So the obtained concept is an approximation to a potential target concept. We discuss the model of this concept learning, sample complexity of its hypothesis space and PAC-learnability of its target concept class.

关键词： Rough Set Concept learning Sample Complezity PAC-Learnability

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Mechatronics, robotics and components for automation and control IFAC milestone report

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IFAC Proceedings Volumes 2008年第2期41卷 10800-10809页

作者： S. Boverie Dongil Dan Cho H. Hashimoto M. Tomizuka Wang Wei D. Zühlke Continental Automotive France BP 83649 1 Ave.Paul Ourliac. F-31036 Toulouse CEDEX. France School of Electrical Engineering and Computer Science Seoul National University Kwanak-ku Seoul 151-742 Korea Institute of Industrial Science University of Tokyo 4-6-1 Komaba Meguro-ku Tokyo 153-8505 JAPAN Department of Mechanical Engineering University of California Berkeley CA 94720-1740 USA Research Center of Information and Control Dalian University of Technology Dalian 116024 P.R. China German Research Center for Artificial Intelligence DFKI GmbH Center for Human-Machine-Interaction PO Box 3049 D-67653 Kaiserslautern Germany

This paper presents a general overview of the technological fields of mechatronic, robotic, components for automation and control. Five technical areas are considered: component and instruments, mechatronic, robotics, cost oriented automation and human-machine systems. The paper addresses their current key problems and the recent major accomplishments. At last the most promising forecasted development and applications are considered.

关键词： Component for control Mechatronic systems Robotics Cost oriented automation Human-machine systems

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CLEER - AN AI-SYSTEM DEVELOPED TO ASSIST EQUIPMENT ARRANGEMENTS ON WARSHIPS

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NAVAL ENGINEERS JOURNAL 1989年第3期101卷 127-137页

作者： ZHOU, HH SILVERMAN, BG SIMKOL, J Dr. H. Harry Zhou:is a research professor at the Institute for Artificial Intelligence of The George Washington University. Dr. Zhou received his master's degree and Ph.D. in computer science from Vanderbilt University in 1984 and 1987 respectively. He did his dissertation in the fields of artificial intelligence analogical reasoning and machine learning. His research interests include: classifier systems genetic algorithms learning by analogy inductive learning adaptive expert systems automated knowledge acquisition and adaption. He is also interested in data base design programming languages mental modeling and software engineering. Dr. Barry G. Silverman:is director of the Institute for Artificial Intelligence and a professor at the Engineering Administration Department of The George Washington University. He is also president of IntelliTek Inc. an AI consulting firm. Dr. Silverman received the B.S.E. M.S.E. and Ph.D. degrees from the University of Pennsylvania. He has been a principal developer of four generic AI products as well as eight AI applications. Since 1979 he has written over 100 papers and reports on these AI efforts. Joel Simkol:is a research scientist currently engaged in designing expert system architectures to support electronic warfare vulnerability analyses threat assessments shipboard topside antenna arrangements and C3countermeasures employment. Mr. Simkol's work in applying expert systems technology to electromagnetic interference and to spectrum management has generated increased interest and participation from all branches of government agencies.

This paper describes a modularized AI system being built to help improve electromagnetic compatibility (EMC) among shipboard topside equipment and their associated systems. CLEER is intended to act as an easy to use integrator of existing expert knowledge and pre-existing data bases and large scale analytical models. Due to these interfaces; to the need for portability of the software; and to artificial intelligence related design requirements (such as the need for spatial reasoning, expert data base management, model base management, track-based reasoning, and analogical (similar ship) reasoning) it was realized that traditional expert system shells would be inappropriate, although relatively off-the-shelf AI technology could be incorporated. In the same vein, the rapid prototyping approach to expert system design and knowledge engineering was not pursued in favor of a rigorous systems engineering methodology. The critical design decisions affecting CLEER's development are summarized in this paper along with lessons learned to date all in terms of “how,” “why,” and “when” specific features are being developed.

关键词： artificial intelligence

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FIRE MAIN CONTROL

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NAVAL ENGINEERS JOURNAL 1985年第6期97卷 35-48页

作者： MALKOFF, DB MOY, MC WILLIAMS, HL Dr. Donald B. Malkoff majored in physics as an undergraduate at Harvard University. He received an M.D. degree from the University of Pittsburgh School of Medicine in 1960. This was followed by an internship and residency in neurology at University Hospital in Ann Arbor Michigan. He spent several years at the National Institutes of Health engaged in gerontology research has practiced and taught clinical neurology and in 1983 received an M.S. degree in computer science at the University of California San Diego. Currently Dr. Malkoff is employed by the Navy Personnel Research and Development Center in San Diego California where he is senior investigator in a human factor/computer display-and-control project involving the DDG-51 gas turbine propulsion unit. He is a member of the American Academy of Neurology the Society for Neuroscience the American Association for Artificial Intelligence and the Association for Computing Machinery. Dr. Malkoff is certified by the American Board of Psychiatry and Neurology has taught computer science at UCSD and published in several research areas including magnetoencephalography and electron microscopy. His basic interests are in the areas of artificial intelligence and learning expert systems particularly as they apply to the problems of fault-detection and control. Dr. Melvyn C. Moy received his undergraduate training in mathematics and chemistry at the University of Texas Austin. He studied experimental psychology at the University of Wisconsin Madison receiving his M.S. in 1970 and Ph.D. in 1972. He served as an assistant professor at the University of South Dakota where he taught experimental design and methodology for a year before joining the Navy Personnel Research and Development Center in 1973. His work and research since then spans across many application areas such as the development of manpower planning models for the Navy the design of operational decision aids the human engineering of interactive large-scale war gaming systems and the evaluation o

The ship fire main has undergone considerable development throughout the past 2,000 years, resulting in a system that is critical both for normal ship function and for ship survivability in emergencies. Because of its complexity, the modern firemain system is highly vulnerable to malfunction and to damage during combat. Firemain fault detection and fault recovery are currently conducted manually by damage control teams. The advantages and disadvantages of this method are discussed, and alternative methods of fault detection and recovery are explored. An interactive computer program is introduced which uses central control over remotely situated valves to facilitate fault detection and recovery, significantly reducing recovery-time and manpower requirements. These reductions may result in savings of lives, ship systems, and ships themselves. The computer program is based upon an algorithm which is, in effect, a prescription that can be followed manually by the operator or be completely automated. The color graphic display which is used for monitoring can also be utilized for the training of damage control operators or for the evaluation of other algorithms for firemain control. Alternative firemain hardware and configurations could lead to even more efficient methods of fault detection and recovery as well as improved firemain water supply management in general.

关键词： NAVAL VESSELS

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