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IEEE Symposium on intelligent Vehicle

作者： Shiliang Sun Guoqiang Yu Changshui Zhang State Key Laboratory of Intelligent Technology and Systems Department of Automation Tsinghua University Beijing China

ISBN: (纸本)0780383109

Short-term traffic flow forecasting is an important problem in the research area of intelligent transportation system. In practical situations, flow data may be incomplete, that is, partially missing or unavailable, where few methods could implement forecasting successfully. A method called Sampling Markov Chain is proposed to deal with this circumstance. In this paper, the traffic flow is modeled as a high order Markov Chain; and the transition probability from one state to the other state is approximated by Gaussian Mixture Model (GMM) whose parameters are estimated with Competitive Expectation Maximum (CEM) algorithm. The incomplete data in forecasting the trend of Markov Chain is represented by enough points sampled using the idea of Monte Carlo integration. Experimental results show that the Sampling Markov Chain method is applicable and effective for short-term traffic flow forecasting in case of incomplete data.

关键词： Sampling methods Traffic control intelligent transportation systems Predictive models Monte Carlo methods Parameter estimation state estimation Sun Prediction methods Neural networks

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Learning probabilistic kernel feature subspace with side-information for classification

Learning probabilistic kernel feature subspace with side-inf...

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International Joint Conference on Neural Networks (IJCNN)

作者： Jianguo Lee Changshui Zhang Zhaoqi Bian State Key Laboratory of Intelligent Technology and Systems Department of Automation Tsinghua University Beijing Beijing China

ISBN: (纸本)0780383591

Kernel PCA is an efficient method for nonlinear feature extraction. We address two issues in kernel PCA based feature extraction and classification. First, it extracts features without utilizing sample label information. Second, it does not provide a practical means to choose the dimensionality for principal subspace. In this paper, one kind of side-information is incorporated into kernel PCA to solve the first problem. And a complete probabilistic density function is estimated in kernel space so that the choice of dimensionality for principal subspace becomes less important. The proposed model is named probabilistic kernel feature subspace (PKFS). Experiments show that it achieves promising performance and outperforms many other algorithms in classification.

关键词： Kernel Principal component analysis Feature extraction Data mining Support vector machines Machine learning algorithms Laboratories intelligent systems automation Electronic mail

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Information fusion in face and fingerprint identity verification system

Information fusion in face and fingerprint identity verifica...

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International Conference on Machine Learning and Cybernetics (ICMLC)

作者： Chun-Hong Jiang Guang-Da Su The State Key Laboratory of Intelligent Technology and System Electronic Engineering Department Tsinghua University Beijing Beijing China

ISBN: (纸本)0780384032

Biometric based person identity verification is gaining more and more attention. It has been shown that combining different biometric modalities enables to achieve better performances than single modality. So as to improve the verification accuracy, this paper combines face and fingerprint for person identity verification. And some multimodal biometric information fusion strategies, includes sum rule (SR), weighted sum rule (WSR), Fisher linear discriminant analysis (FLDA) and support vector machine (SVM) are evaluated, furthermore, a new method for data normalization in verification system is proposed in this paper. Experiment results prove the effectiveness of fusion of multiple biometrics compared with single biometric, and also the better verification performance by adopting the new data normalization method. The SVM, SR, WSR and FLDA fusion methods present a decreasing performance in our experiment.

关键词： Fingerprint recognition Biometrics Face detection Support vector machines Feature extraction Performance analysis Strontium Meteorological radar Algorithm design and analysis Laboratories

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A semi-supervised classification method based on transduction of labeled data

A semi-supervised classification method based on transductio...

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IEEE Conference on Cybernetics and intelligent systems

作者： Shiliang Sun Changshui Zhang Naijiang Lu Fei Xiao State Key Laboratory of Intelligent Technology and Systems Department of Automation Tsinghua University Beijing China Cogent Biometrics Identification Technology Company Limited Shanghai China

ISBN: (纸本)0780386434

The semi-supervised classification problem with partially labeled data is very important in the research area of pattern recognition and machine learning. In this paper, an approach based on transduction of labeled data is proposed to improve current classification methods. The general knowledge about the attribute of data distribution is used to carry out transduction. Employing this kind of knowledge, the commonly existent mode of the distribution corresponding to each labeled sample can be effectively found by mean shift, and the data at the mode can be regarded as having the same label with the original labeled sample with high confidence. Using the mode data instead of the original labeled data for classification can be capable of improving classification performance. Encouraging experimental results both on synthetic data and real-world handwritten characters validate the applicability and effectiveness of the approach

关键词： Laboratories Pattern recognition Machine learning Sun intelligent systems Learning systems automation Humans Testing Proteins

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Reinforcement learning based on spatial and temporal association of states

Reinforcement learning based on spatial and temporal associa...

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IEEE International Conference on systems, Man and Cybernetics

作者： Xiao-Dong Zhuang Qing-Chun Meng Bo Yin Yun Gao Computer Science Department Ocean University of China Qingdao China State Key Laboratory of Intelligent Technology and System Tsinghua University Beijing China

ISBN: (纸本)0780385675

In this paper, mechanisms in human learning are incorporated into the reinforcement learning to improve the learning efficiency. A new learning method is presented based on the spatial and temporal association of states, which is inspired by the analogy and recall in human learning. The fuzzy state is proposed to represent the spatial association of states in the state space. The delayed optimization of the control process is proposed for learning with temporally correlated states. In the experiment, the proposed method is applied to a maze problem, which shows that the proposed method has improved learning performance.

关键词： state-space methods Learning systems Humans Stochastic processes Delay Machine learning Process control Optimal control Computer science Oceans

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Using fuzzy sets and genetic algorithm for Chinese verb classification

Using fuzzy sets and genetic algorithm for Chinese verb clas...

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World Congress on intelligent Control and automation (WCICA)

作者： Wei Li Chunfa Yuan Kam-Fai Wong Chun-Hung Cheng Wenjie Li State Key Laboratory of Intelligent Technology and System Tsinghua University Beijing China Department of SE&EM Chinese University of Hong Kong New Territories Hong Kong China Department of Computing Hong Kong Polytechnic University Hong Kong China

ISBN: (纸本)0780382730

Verb classification is very important for temporal information analysis and semantic understanding. In English, this topic has been fully studied. However, there are few works and no systematic approaches in Chinese up to now. We propose a new approach using fuzzy sets and genetic algorithm for Chinese verb classification. Its contribution lies in two aspects: it (a) provides a flexible and systematic framework for solving this problem; and (b) achieves high precision in experiments.

关键词： Fuzzy sets Genetic algorithms Information analysis intelligent systems Laboratories Helium

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Erratum to “Distance metric learning by knowledge embedding” [Pattern Recognition 37 (1) (2004) 161–163]

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Pattern Recognition 2004年第4期37卷 855-855页

作者： Yun Gang Zhang Chang Shui Zhang David Zhang State Key Laboratory of Intelligent Technology and Systems Department of Automation Tsinghua University Beijing 100084 China Department of Computing The Hong Kong Polytechnic University Hong Kong

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Grading learning for blind source separation

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Science in China(Series F) 2003年第1期46卷 31-44页

作者：张贤达朱孝龙保铮 Key Laboratory for Radar Signal Processing Xidian University Department of Automation State Key Laboratory of Intelligent Technology and Systems Tsinghua University Beijing 100084 China

By generalizing the learning rate parameter to a learning rate matrix, this paper proposes a grading learning algorithm for blind source separation. The whole learning process is divided into three stages: initial stage, capturing stage and tracking stage. In different stages, different learning rates are used for each output component, which is determined by its dependency on other output components. It is shown that the grading learning algorithm is equivariant and can keep the separating matrix from becoming singular. Simulations show that the proposed algorithm can achieve faster convergence, better steady-state performance and higher numerical robustness, as compared with the existing algorithms using fixed, time-descending and adaptive learning rates.

关键词： blind source separation independent component analysis neural computation adaptive learning.

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A new algorithm for character segmentation of license plate

A new algorithm for character segmentation of license plate

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IEEE Symposium on intelligent Vehicle

作者： Yungang Zhang Changshui Zhang State Key Laboratory of Intelligent Technology and System Department of Automation Tsinghua University Beijing China

ISBN: (纸本)0780378482

Character segmentation is an important step in License Plate Recognition (LPR) system. There are many difficulties in this step, such as the influence of image noise, plate frame, rivet, space mark, and so on. This paper presents a new algorithm for character segmentation, using Hough transformation and the prior knowledge in horizontal and vertical segmentation respectively. Furthermore, a new object enhancement technique is used for image preprocessing. The experiment results show a good performance of this new segmentation algorithm.

关键词： Licenses Image segmentation Pixel Laboratories intelligent systems Space technology automation Character recognition Histograms Image analysis

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Reply networks on a bulletin board system

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Physical Review E 2003年第3期67卷 036117-036117页

作者： Kou Zhongbao Zhang Changshui State Key Laboratory of Intelligent Technology and Systems Department of Automation Tsinghua University Beijing 100084 People’s Republic of China

Using data from a bulletin board system (BBS), we constructed reply networks for various boards, which can be considered as social networks connecting people of the same interests. In these networks, identifications (IDs) are treated as nodes and reply articles set up links. We investigated some statistics on these reply networks, such as clustering coefficients, characteristic path lengths, degree distributions, etc., and showed small-world characteristics and scale-free degree distributions of these reply networks. Then we put forward a model of interest space, which is the basis of the reply networks. We indicated that the hierarchical and clustering structure of the interest space, together with overlapping interests of IDs not only result in small-world characteristics of reply networks on BBS, but also give rise to preferential attachment, which is a popular explanation for scale-free features.

关键词： Statistical mechanics

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