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Assembled matrix distance metric for 2DPCA-based face and palmprint recognition

Assembled matrix distance metric for 2DPCA-based face and pa...

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international conference on machine learning and Cybernetics (icmlc)

作者： Wang-Meng Zuo Kuan-Quan Wang David Zhang School of Computer Science and Technology Harbin Institute of Technology Harbin Heilongjiang China Biometrics Research Centre Department of Computing Hong Kong Polytechnic University Kowloon Hong Kong China

Two-dimensional principal component analysis (2DPCA) is a novel image representation approach recently developed for image recognition. One advantage of 2DPCA is that it can extract feature matrix using a straightforward image projection technique. In this paper, we propose an assembled matrix distance metric (AMD) to measure the distance between two feature matrices. To test the efficiency of the proposed distance measure, we use two image databases, the ORL face and the PolyU palmprint. The experimental results show that the assembled matrix distance metric is very effective in 2DPCA based image recognition.

关键词： Assembly Face recognition Principal component analysis Image recognition Image databases Image representation Feature extraction Covariance matrix Optimized production technology Spatial databases

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Using improved SVM decision tree to classify HRRP

Using improved SVM decision tree to classify HRRP

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international conference on machine learning and Cybernetics (icmlc)

作者： Xiao-Dan Wang Chong-Ming Wu Department of Computer Engineering Missile Institute Air Force Engineering University SanYuan Shaanxi China

Radar target classification by using high resolution range profile (HRRP) as features has been studied in this paper. Support vector machine (SVM) has been used in range profile classification for its good generalization ability for the pattern classification problem with high feature dimension and small training set. In order to perform multi-class classification, decision-tree-based SVM has been studied. Based on the analysis of the structure and the classification performance of the SVM decision tree, and by introducing the defined separability measure that based on the distribution of the training samples into the formation of the decision tree, an improved algorithm for SVM decision tree has been proposed. The scheme of using the improved algorithm for SVM decision tree to classify HRRP has been studied. Experiments using the simulated range profile datasets prove the effectiveness of our scheme.

关键词： Support vector machines Support vector machine classification Decision trees Classification tree analysis Radar Performance analysis Algorithm design and analysis Military computing Missiles Electronic mail

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Short term load forecasting model using support vector machine based on artificial neural network

Short term load forecasting model using support vector machi...

引用

international conference on machine learning and Cybernetics (icmlc)

作者： Dong-Xiao Niu Qiang Wanq Jin-Chao Li School of Business Administration North China Electric Power University Baoding P.R.China North China Electric Power University - Baoding Campus Baoding Hebei CN Sch. of Bus. Adm. North China Electr. Power Univ. Baoding China

A new sample preprocessing method is put forward in this paper. Firstly, the data points are classified into three types as the following: the high load type, the medium load type and the low load type; then, the artificial neural network is adopted to forecast the load type of the predict point; finally a support vector machine forecasting model is created on the basis of data points whose load type is the same as the predict point. It is the first time for artificial neural network to be combined with support vector machine in short term load forecasting. The practical examples show that the model established in this paper is better than other methods in forecasting accuracy and computing speed.

关键词： Load forecasting Load modeling Predictive models Support vector machines Artificial neural networks Economic forecasting Support vector machine classification Neural networks machine learning algorithms Artificial intelligence

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Classifying visual field loss in glaucoma through baseline matching of stable reference sequences

Classifying visual field loss in glaucoma through baseline m...

引用

international conference on machine learning and Cybernetics (icmlc)

作者： Meng Turpin Lazarescu Ivins Department of Computing Curtin University of Technology Perth WA Australia School of Computing Science and Information Technology Melbourne VIC Australia

Glaucoma is a common disease of the eye that often results in partial blindness. The main symptom of glaucoma is progressive loss of sight in the visual field over time. The clinical management of glaucoma involves monitoring the progress of the disease using a sequence of regular visual field tests. However, there is currently no universally accepted standard method for classifying changes in the visual field test data. Sequence matching techniques typically rely on similarity measures. However, visual field measurements are very noisy, particularly in people with glaucoma. It is therefore difficult to establish a reference data set including both stable and progressive visual fields. This paper proposes a method that uses a "baseline" computed from a query sequence, to match stable sequences in a database of visual field measurements collected from volunteers. The purpose of the new method is to classify a given query sequence as being stable or progressive. The results suggest that the new method gives a significant improvement in accuracy for identifying progressive sequences, though there is a small penalty for stable sequences.

关键词： Sequences Testing Diseases Blindness Visual databases Optical signal processing Medical treatment Information technology Electronic mail Monitoring

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A neural network regression model for tropical cyclone forecast

A neural network regression model for tropical cyclone forec...

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international conference on machine learning and Cybernetics (icmlc)

作者： Liu Bo Feng Department of Computing Hong Kong Polytechnic University Hong Kong China

In recent years a large amount of literature has evolved on the usage of artificial neural network (ANNs) for weather forecasting, particularly because of ANNs' ability to model an unspecified nonlinear relationship of various meteorological variables. In this paper we proposed a dynamic competitive neural network classifier to predict the maximum potential intensity (MPI) of a given tropical cyclone, based on a 10-year period of Western North Pacific tropical cyclones and monthly mean sea surface temperature (MSST). A procedure to select most significant-correlated attributes of tropical cyclones is designed to for fast and accurate neural network training. A binary trigger is adopted to adjust the structure of the network layers. To justify the performance, we carry out a set of experiments to prove that our proposed model is promising.

关键词： Neural networks Predictive models Tropical cyclones Artificial neural networks Ocean temperature Weather forecasting Sea surface Storms Satellites Biological neural networks

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Using complex linguistic features in context-sensitive text classification techniques

Using complex linguistic features in context-sensitive text ...

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international conference on machine learning and Cybernetics (icmlc)

作者： Wong Lee Yeung Department of Computing Hong Kong Polytechnic University Kowloon Hong Kong China

Text classification (TC) is the task to automatically classify documents based on learned document features. Many popular TC models use simple occurrence of words in a document as features. They also commonly assume word occurrences to be statistically independent in their design. Although it is obvious that such assumption does not hold in general, these TC models have been robust and efficient in their task. Some recent studies have shown context-sensitive TC approaches, which take into consideration contexts in the form of word co-occurrences, have been able to perform better in general. On the other hand, there have been many studies in the use of complex linguistic or semantic features instead of simple word occurrences as features for information retrieval and classification tasks. While these complex features may intuitively have more relevance to the tasks concerned, results of these studies on their effectiveness have been mixed and not been conclusive. In this paper we present our investigation on the use of some complex linguistic features with context-sensitive TC method. Our experiment results show some potential advantages of such approach.

关键词： Text categorization Robustness Electronic mail Information retrieval Text processing Feature extraction machine learning algorithms machine learning Cybernetics

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A Novel Model Based on Negotiation Axiom System of Capability

A Novel Model Based on Negotiation Axiom System of Capabilit...

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international conference on machine learning and Cybernetics (icmlc)

作者： Wei-Jin Jiang Yu-Hui Xu Yu-Sheng Xu Department of Computer Zhuzhou Institute of Technology Zhuzhou China Department of Information & Computing Science Zhuzhou Institute of Technology Zhuzhou China College of Mechanical Engineering and Applied Electronics Beijing University of Technology Beijing China

With the information explosion speeds up the increasing of computing complexity rapidly, the traditional centralized computing patterns are under great pressure to process those large-scale distributed information. However, the agent-based computation and high-level interaction protocols foster the modern computation and distributed information processing successfully. The multi-agent system (MAS) plays an important role in the analysis of the human-interaction theory and model building. This study focuses on the formal description of MAS, the conflict-resolving mechanisms and the negotiation in MAS. In this paper, we present a formalism for reasoning about the interaction of multi-agent system, which is MAS model based on negotiation axiom system of capability and thought (CTN system). We first present the semantic frame and define the global states, the class of the CTN system, the running and reachable states for the CTN system. Then we present the axiomatic of negotiating logic. We also prove the Logical Consistency and Completeness of the CTN system and discuss the conflict-resolving mechanisms. The semantic of CTN model is intuitive, rational and natural, and the CTN model is a formal description by integrated the ability and thought status of Agent.

关键词： Logic Distributed computing Multiagent systems Educational institutions Mechanical engineering Electronic mail Explosions Large-scale systems Protocols Information processing

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Coal gangue online recognition and automation selection system based on ARM and CAN bus

Coal gangue online recognition and automation selection syst...

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international conference on machine learning and Cybernetics (icmlc)

作者： Xian-Min Ma Xiao-Ru Song Department of Electrical and Control Engineering Xi'an University of Science and Technology Xi'an China

ARM (advanced RISC machines) microcontroller is widely used in lots of fields with characteristics of high-performance, small volume, low power dissipation and low-cost exactly; CAN (controller area network) bus is a bus system. It is especially suited for networking "intelligent" I/O devices. In this paper, it is introduced to explore a new method for the coal dry selection that the coal gangue online recognition and automation selection system based on the newly ARM microcontroller (AT91M40800) and CAN bus (SJA1000) in brief. CPLD technology (EPM7128) is used to modulate time sequence and online update program. Firstly, the digital image processing technology and arithmetic is expatriated in particular. Then the design of the interface circuits is in detail discussed between the ARM microcontroller and the CAN bus, and last the flow diagram of the software is given. Experiments show that the design is successful.

关键词： Automation Microcontrollers Reduced instruction set computing Power dissipation Automatic control Control systems Intelligent networks machine intelligence Modulation coding Digital images

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An SVM-based soccer video shot classification

An SVM-based soccer video shot classification

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international conference on machine learning and Cybernetics (icmlc)

作者： Yi-Hua Zhou Yuan-Da Cao Long-Fei Zhang Hong-Xin Zhang Department of Computer Science School of Information Science and Technology Beijing Institute of Technology Beijing China School of Computer Software Beijing Institute of Technology Beijing China Institute of Communication Network Colligate Technology Beijing University of Posts and Telecommunications Beijing China

Video shot classification is an important component of content-based video retrieval. It is also a basic step towards video abstract, event detection and content filtering. According to the characters of shots for soccer videos, an integrating color and edge distribution shot classification method is presented. First, A GMM model is trained for grass pixel. And then the grass distribution is computed based on the trained GMM. Due to the sensitivity to light, field and time for color features, edge distribution is computed through canny operator. These features are reasonable complementarities to color features and have no sensitivity to light, field and time. Integrating two types of features embodies main characters of different shot types. One-against-others SVMs are designed for shots classification. Experiments show that our method performs better results.

关键词： Filtering Event detection Distributed computing Computer science Information science Computer networks Telecommunication computing Software Communication networks Communications technology

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A novel feature extraction algorithm

A novel feature extraction algorithm

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international conference on machine learning and Cybernetics (icmlc)

作者： Shi-Fei Ding Zhong-Zhi Shi Vun-Cheng Wang Shu-Shan Li College of Information Science and Engineering Shandong Agricultural University Taian China Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy and Sciences Beijing China College of Information Science and Engineering Shandong University of Science and Technology Qingdao China

Feature extraction or selection is one of the most important steps in pattern recognition or pattern classification, data mining, machine learning and so on. In this paper, we introduce the information theory, propose a new concept of probability information distance (PID) and prove that the PID satisfies four requests of axiomatization of the distance. So the PID is a kind of distance measure, which can be used to measure the degree of variation between two random variables. We make the PID be separability criterion of the classes for information feature extraction, and call it PID criterion (PIDC). Based on PIDC, we design a novel algorithm for information feature extraction. Compared with principal components analysis (PCA), correlation analysis etc., the algorithm put forward in this paper had regarded for the class information, and so it is a kind of supervised algorithm of feature extraction. The experimental results demonstrate that the algorithm is valid and reliable, and it provides a new research approach for feature extraction, data mining and pattern recognition.

关键词： Feature extraction Pattern recognition Data mining Algorithm design and analysis Principal component analysis machine learning algorithms Pattern classification machine learning Information theory Random variables

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