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Extended topic map: Knowledge collaborative building for distributed knowledge resource

Extended topic map: Knowledge collaborative building for dis...

引用

IEEE Symposium on Network Operations and Management

作者： Huimin Lu Boqin Feng Xi Chen School of Electronic and Information Engineering Xi''an Jiaotong University Xi'an China Department of Machine Learning School of Computer Science Carnegie Mellon University Pittsburgh USA

ISBN: (纸本)9781424453665

The characteristics of information resources are distributed, autonomous, heterogeneous and full of semantics in complex knowledge-based organizations at the era of knowledge economy. In order to realize efficient knowledge acquisition, representation, exchange, and sharing, we propose a novel concept of extended topic map, which extends the conventional topic map in structure. It embodies the multi-level, multi-granularity and other inherent relevant characteristics of knowledge. With the extended topic map as infrastructure, the framework of knowledge collaborative building in distributed scenario is achieved by the following four stages: knowledge extraction, local extended topic map generation, similarity calculation and global extended topic map generation. We give the key technologies of each stage, which take advantage of extended topic map-based techniques such as comprehensive information theory based similarity measure and merging strategies. Finally, an example of multi-resource knowledge integration based on extended topic map is given to elaborate the knowledge collaborative building process.

关键词： Collaboration Buildings Semantic Web Ontologies XML Information resources Merging Knowledge engineering Knowledge acquisition Knowledge management

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Self-adaptive fuzzification in fuzzy decision tree induction

Self-adaptive fuzzification in fuzzy decision tree induction

引用

International Conference on machine learning and Cybernetics (ICMLC)

作者： Xiao-Dong Dai Lin-Qing Gao Chun-Ru Dong Department of Mathematics and Computer Science Hebei University Baoding China Graduate School Hebei University Baoding China Machine Learning and Cybernetics Research Center School of Computer Science and Engineering South China University of Technology Guangzhou China

One of the most important issues in fuzzy decision tree learning is the fuzzification of input data. This paper proposes a self-adaptive data fuzzification algorithm based on the self-organizing map (SOM) technology, which can automatically determine the number and coordinates of centers in triangular membership functions. Then the membership degree of each sample to all fuzzy subsets can be calculated. Finally, a fuzzy decision tree can be learned from the fuzzified training samples by any selected fuzzy decision tree heuristic algorithm. Experimental results on UCI data set iris show that the new approach outperform the traditional fuzzification methods.

关键词： Decision trees Accuracy Training Neurons Classification algorithms machine learning Fuzzy sets

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Excel in mathematics: Applications of calculus

Excel in mathematics: Applications of calculus

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2010 ASEE Annual Conference and Exposition

作者： Young, Cynthia Georgiopoulos, Michael Crouse, Tace Geiger, Cherie Islas, Alvaro Hagen, Scott Dagley-Falls, Melissa Ramsey, Patricia Lancey, Patrice Department of Mathematics College of Sciences UCF United States School of Electrical Engineering and Computer Science UCF United States Machine Learning Laboratory UCF United States Faculty Center for Teaching and Learning University of Central Florida United States Department of Chemistry College of Sciences UCF United States Civil Environmental and Construction Department UCF United States Coastal Hydroscience Analysis Modeling and Predictive Simulations Laboratory UCF United States Academic Affairs College of Engineering and Computer Science UCF United States Institutional Research University of Central Florida United States Department of Operational Excellence and Assessment Support University of Central Florida United States

Nationally only 40% of the incoming freshmen STEM majors are successful in earning a STEM degree [1]. The University of Central Florida (UCF) EXCEL program is an NSF funded STEP (science, Technology, Engineering and Mathematics Talent Expansion Program) whose goal is to increase the number of UCF STEM graduates. One of the activities that EXCEL has identified as essential in retaining students in science and engineering disciplines is the development and teaching of special courses at the freshman level, called Applications of Calculus I and Applications of Calculus II, or Apps I and Apps II, respectively. In Apps I and II, science and engineering professors are asked to lecture twice, as guest lectures, and to demonstrate to students where calculus topics appear in upper level science and engineering classes as well as where faculty use calculus in their research programs. This paper outlines the process used in producing the educational materials for Apps I and II courses (textbook, presentations slides, in-class homework assignments, demos) and it also discusses the assessment results pertaining to this specific EXCEL activity. © American Society for Engineering Education, 2010.

关键词： Calculations

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Discretization of continuous-valued attributes in decision tree generation

Discretization of continuous-valued attributes in decision t...

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International Conference on machine learning and Cybernetics (ICMLC)

作者： Wen-Liagn Li Rui-Hua Yu Xi-Zhao Wang Key Laboratory of Machine Learning and Computational Intelligence College of Mathematics and Computer Science Hebei University Baoding China Department of Information Technology Baoding University Baoding China

Decision tree is one of the most popular and widely used classification models in machine learning. The discretization of continuous-valued attributes plays an important role in decision tree generation. In this paper, we improve Fayyad's discretization method which uses the average class entropy of candidate partitions to select boundaries for discretization. Our method can reduce the number of candidate boundaries further. Here we also propose a generalized splitting criterion for cut point selection and prove that the cut points are always on boundaries when using this criterion. Along with the formal proof, we present empirical results that the decision trees generated by using such criteria are similar on several datasets from the UCI machine learning Repository.

关键词： Entropy machine learning Impurities Indexes Classification tree analysis

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Cooperative Co-evolution for large scale optimization through more frequent random grouping

Cooperative Co-evolution for large scale optimization throug...

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Congress on Evolutionary Computation

作者： Mohammad Nabi Omidvar Xiaodong Li Zhenyu Yang Xin Yao Evolutionary Computing and Machine Learning Group (ECML) the School of Computer Science and IT RMIT University Melbourne VIC Australia Nature Inspired Computation and Application Laboratory Department of Computer Science and Technology University of Science and Technology Hefei Anhui China Centre of Excellence of Research in Computational Intelligence and Applications (CERCIA) School of Computer Science University of Binningham Birmingham UK

In this paper we propose three techniques to improve the performance of one of the major algorithms for large scale continuous global function optimization. Multilevel Cooperative Co-evolution (MLCC) is based on a Cooperative Co-evolutionary framework and employs a technique called random grouping in order to group interacting variables in one subcomponent. It also uses another technique called adaptive weighting for co-adaptation of subcomponents. We prove that the probability of grouping interacting variables in one subcomponent using random grouping drops significantly as the number of interacting variables increases. This calls for more frequent random grouping of variables. We show how to increase the frequency of random grouping without increasing the number of fitness evaluations. We also show that adaptive weighting is ineffective and in most cases fails to improve the quality of found solution, and hence wastes considerable amount of CPU time by extra evaluations of objective function. Finally we propose a new technique for self-adaptation of the subcomponent sizes in CC. We demonstrate how a substantial improvement can be gained by applying these three techniques.

关键词： Optimization Probability computer science Electronic mail Space exploration Computational modeling Software

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Accounting for network effects in neuronal responses using L1 regularized point process models

Accounting for network effects in neuronal responses using L...

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24th Annual Conference on Neural Information Processing Systems 2010, NIPS 2010

作者： Kelly, Ryan C. Smith, Matthew A. Kass, Robert E. Lee, Tai Sing Computer Science Department Center for the Neural Basis of Cognition Carnegie Mellon University Pittsburgh PA 15213 United States University of Pittsburgh Center for the Neural Basis of Cognition Pittsburgh PA 15213 United States Department of Statistics Center for the Neural Basis of Cognition Pittsburgh PA 15213 United States Machine Learning Department Carnegie Mellon University Pittsburgh PA 15213 United States

ISBN: (纸本)9781617823800

Activity of a neuron, even in the early sensory areas, is not simply a function of its local receptive field or tuning properties, but depends on global context of the stimulus, as well as the neural context. This suggests the activity of the surrounding neurons and global brain states can exert considerable influence on the activity of a neuron. In this paper we implemented an L1 regularized point process model to assess the contribution of multiple factors to the firing rate of many individual units recorded simultaneously from V1 with a 96-electrode "Utah" array. We found that the spikes of surrounding neurons indeed provide strong predictions of a neuron's response, in addition to the neuron's receptive field transfer function. We also found that the same spikes could be accounted for with the local field potentials, a surrogate measure of global network states. This work shows that accounting for network fluctuations can improve estimates of single trial firing rate and stimulus-response transfer functions.

关键词： Neurons

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Wireless sensor networks for Home Appliance Energy Management based on ZigBee technology

Wireless sensor networks for Home Appliance Energy Managemen...

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International Conference on machine learning and Cybernetics (ICMLC)

作者： Jian-Jian Wang Shuo Wang Department of Electronics and Communication Engineering North China Electric Power University Baoding China Machine Learning Center Faculty of Mathematics and Computer Science HeBei University Baoding China

Wireless Sensor Networks for Home Appliance Energy Management based on ZigBee technology is introduced in this paper. The aim of this research is to develop a real-time, low-cost, low power consumption and better reliable system for monitoring home appliance. Based on system analysis, hardware architecture design and underlying software development are presented. Experiment testing result demonstrates that this system is suitable for home appliance energy management and it makes electric scheduling and management more convenient and flexible. Moreover, it can be extended to a whole building or community.

关键词： Wireless sensor networks Base stations Home appliances Power demand Monitoring Zigbee Switches

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Moving average crossovers for short-term equity investment with L-GEM based RBFNN

Moving average crossovers for short-term equity investment w...

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International Conference on machine learning and Cybernetics (ICMLC)

作者： Gao-Yang Cai Wing W. Y. Ng Patrick P. K. Chan Michael Firth Daniel S. Yeung Machine Learning and Cybernetics Research Center School of Computer Science and Engineering South China University of Technology Guangzhou China Department of Finance and Insurance Lingnan University Hong Kong China

The Shenzhen Stock Exchange (SZSE) market is young and energetic. Evidence exists that the returns from emerging markets like the SSE are influenced by a different set of factors than those of developed markets. The Moving Average (MA) crossover technique is one of the popular technical analysis tools used by investors in financial markets. However, not all MA crossovers give accurate predictions of uptrends in stock prices. This motivates us to investigate the use of MA crossovers in short-term investment with Radial Basis Function Neural Network (RBFNN) trained via a minimization of the Localized Generalization Error (L-GEM). Experiments show that the proposed method can yield statistically significant profits when compared with a random investment strategy.

关键词： Investments Artificial neural networks Stock markets Neurons Training Finance machine learning

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Combining Rule Induction and Reinforcement learning: An Agent-based Vehicle Routing

Combining Rule Induction and Reinforcement Learning: An Agen...

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International Conference on machine learning and Applications (ICMLA)

作者： Bartlomiej Śniezyński Wojciech Wójcik Jan D. Gehrke Janusz Wojtusiak Department of Computer Science AGH University of Science and Technology Krakow Krakow Poland Center for Computing and Communication Technologies – TZI Universität Bremen Bremen Bremen Germany Machine Learning and Inference Laboratory George Mason University Fairfax VA USA

Reinforcement learning suffers from inefficiency when the number of potential solutions to be searched is large. This paper describes a method of improving reinforcement learning by applying rule induction in multi-agent systems. Knowledge captured by learned rules is used to reduce search space in reinforcement learning, allowing it to shorten learning time. The method is particularly suitable for agents operating in dynamically changing environments, in which fast response to changes is required. The method has been tested in transportation logistics domain in which agents represent vehicles being routed in a simple road network. Experimental results indicate that in this domain the method performs better than traditional Q-learning, as indicated by statistical comparison.

关键词： learning Roads Multiagent systems Plasmas Computational modeling Logistics Vehicles

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A new instance selection algorithm based on contribution for nearest neighbour classification

A new instance selection algorithm based on contribution for...

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International Conference on machine learning and Cybernetics (ICMLC)

作者： Yong-Hua Cai Bo Wu Yu-Lin He Ye Zhang Department of Mathematics and Computer Science Hebei Normal University of Nationalities Chengde Hebei China Automation Department Chengdu Iron & Steel Branch Hebei Iron and Steel Group Company Limited Chengde Hebei China Key Laboratory of Machine Learning and Computational Intelligence College of Mathematics and Computer Science Hebei University Baoding Hebei China

Nearest Neighbor Classifier is one of the most classical lazy learning schemes. The basic nearest neighbor classifiers suffer from the common problem that the instances used to train the classifier are all stored indiscriminately, and as a result, the required memory storage is huge and response time becomes slow with a large database. In this paper, a new Instances Selection algorithm based on Classification Contribution Function shortly named ISCC is presented. In this algorithm, a function is introduced to evaluate the classification ability of the instances. For each instance, the function considers its contribution to the neighbor instances not only with same class but also with different class. Then an instance with the highest value of Classification Contribution Function is added to the condensed subset and the instances which can be classified correctly are deleted in each iteration. This process is repeated until the subset is no longer getting larger. The time complexity of ISCC is O (in 2 ). The experimental results on two artificial databases and some real databases demonstrate the effectiveness and the feasibility of the proposed algorithm. Compared to traditional methods, such as MCS, ICF and ENN, the condensed sets obtained by ISCC is superior in storage and classification accuracy.

关键词： Classification algorithms Nearest neighbor searches Training machine learning Accuracy Noise learning

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