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IEEE International Symposium on Geoscience and Remote Sensing (IGARSS)

作者： Ji Wang Wen-kai Lu State Key Laboratory of Intelligent Technology and Systems Tsinghua National Laboratory of Information Science and Technology Department of Automation Tsinghua University Beijing China

Data of towed streamer acquiring suffer from influences of ocean currents and other environment factors. Variations in lateral and time positions of receivers will produce acquisition errors and reduce resolution of stack sections. In this paper, we proposed a method to correct those variations in sub-pixel domain by interpolation and correlate calculation. We applied the method on an actual dataset acquired at an offshore field in China. Results demonstrate the method could correct variations effectively and enhance the consistence and the resolution of the acquired dataset.

关键词： Oceans Interpolation Sonar equipment Tides Laboratories Marine technology Streaming media Reflection intelligent systems Information science

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Model-based bridge recognition in high resolution SAR image

Model-based bridge recognition in high resolution SAR image

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MIPPR 2009 - Automatic Target Recognition and Image Analysis: 6th International Symposium on Multispectral Image Processing and Pattern Recognition

作者： Pei, Deli Sun, Fuchun Wang, Hongqiao Chen, Ning Department of Computer Science and Technology Tsinghua University Beijing 100084 China State Key Lab. of Intelligent Technology and Systems Tsinghua National Laboratory for Information Science and Technology Tsinghua University Beijing 100084 China Xi'an High Tech. Research Institute Xi'an Shanxi 710025 China

ISBN: (纸本)9780819478061

A new bridge recognition method in Synthetic Aperture Radar (SAR) image using bridge model and SVM is presented in this paper. Firstly, water region is extracted from original SAR image by self-adapt segmentation and mathematic morphological method. Then based on bridge model, the bridge candidates in water region can be found out and their texture feature can also be ***, with the usage of SVM, whether these objects are bridges or not can be identified and the real bridges can be mark ed in the SAR image, as well as the corresponding information such as position. Experiment results show that, with the help of water region segmentation and bridge model, the efficiency of the algorithm is greatly promoted, meanwhile, the recognition accuracy is guaranteed by high quality classifier of SVM. © 2009 Copyright SPIE - The International Society for Optical Engineering.

关键词： Synthetic aperture radar

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Incorporate web search technology to solve out-of-vocabulary words in Chinese word segmentation

PACLIC 23 - Proceedings of the 23rd Pacific Asia Conference ...

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PACLIC 23 - Proceedings of the 23rd Pacific Asia Conference on Language, Information and Computation 2009年 2卷 454-463页

作者： Qiao, Wei Sun, Maosong State Key Laboratory of Intelligent Technology and Systems Tsinghua National Laboratory for Information Science and Technology Department of Computer Sci. and Tech. Tsinghua University Beijing 100084 China

ISBN: (纸本)9789624423198

Chinese word segmentation (CWS) is the fundamental technology for many NLPrelated applications. It is reported that more than 60% of segmentation errors is caused by the out-of-vocabulary (OOV) words. Recent studies in CWS show that, statistical machine learning method is, to some extent, effective on solving OOV words. But labeled data is limited in size and unbalanced in content which makes it impossible to obtain all the required knowledge to recognize OOV words. In this paper, large scaled web data is incorporated as knowledge supplement. A framework which combines using web search technology and machine learning method is proposed. For each sentence, basic segmentation is performed using linear-chain Conditional Random Fields (CRF) model. Substrings which CRF model gives low confidence decisions are extracted and sent to search engine to perform web search based word segmentation. Final decision is made by considering both CRF model based segmentation result and that of web search based result. Evaluations are conducted on SIGHAN Bakeoff 2005 and 2006 datasets, showing the effectiveness of the proposed framework on dealing with OOV words. © 2009 by Wei Qiao and Maosong Sun.

关键词： Search engines

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Web Observation from a User Perspective

Web Observation from a User Perspective

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IEEE WIC ACM International Conference on Web Intelligence (WI)

作者： Rongwei Cen Yiqun Liu Min Zhang Liyun Ru Shaoping Ma State Key Laboratory of Intelligent Technology and Systems Tsinghua National Laboratory of Information Science and Technology Department of Computer Science and Technology Tsinghua University Beijing China

How many pages on the Web will be accessed by Web users? This is an interesting question for both Web scientists and industry engineers. To answer this question, User Access Web (UA Web) is described and studied in this paper. With analysis on large scale Web users’ access logs, a sampling procedure is proposed to reduce the bias, and the near-uniform random pages are sampled from the UA Web applying search engine interface and Monte Carlo methods. Experimental results on about 675 million user log entries reveal some properties of the UA Web and the indices of four search engines, e.g. power law distribution, average length of pages, index size of search engines, properties of static and dynamic pages, etc.

关键词： Sampling methods Search engines intelligent agent Conferences intelligent systems Information science Computer science Large-scale systems Web sites Internet

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Information Theoretic Interpretation of Error Criteria

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自动化学报 2009年第10期35卷 1302-1309页

作者： CHEN Ba-Dong HU Jin-Chun ZHU Yu SUN Zeng-Qi Institute of Manufacturing Engineering Department .of Precision Instruments and Mechanology Tsinghua University Beijing 100084 P.R. China State Key Laboratory of Intelligent Technology and Systems Department of Computer Science and Technology Tsinghua University Beijing 100084 P.R. China

Error criteria (or error cost functions) play significant roles in statistical estimation problems. In this paper, we study error criteria from the viewpoint of information theory. The relationships between error criteria and error's entropy criterion are investigated. It is shown that an error criterion is equivalent to the error's entropy criterion plus a Kullback-Leibler information divergence (KL-divergence). Based on this result, two important properties of the error criteria are proved. Particularly, the optimum error criterion can be interpreted via the meanings of entropy and KL-divergence. Furthermore, a novel approach is proposed for the choice of p-power error criteria, in which a KL-divergence based cost is minimized. The proposed method is verified by Monte Carlo simulation experiments.

关键词：误差标准误差成本函数估计熵 KL发散

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Universal Approximation for Takagi-Sugeno Fuzzy systems Combining Statically and Dynamically Constructive Methods-MISO Cases

Universal Approximation for Takagi-Sugeno Fuzzy Systems Comb...

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International Conference on Information Management and Engineering, ICIME

作者： Shi Yu Yan Zeng Qi Sun State Key Laboratory on Intelligent Technology and Systems Tsinghua National Laboratory for Information Science and Technology Department of Computer Science and Technology Tsinghua University Beijing China

ISBN: (纸本)9781424437740

In this paper, a combined method is proposed for providing a new sufficient condition for linear T-S fuzzy systems as an universal approximator in MISO case. Such work extends existing methods in SISO case and is superior to the existing results in MISO case by combining the statically and dynamically constructive methods. After comparing with some other typical methods, detailed theoretical reasoning and numerical example both confirm that the method in this paper will reduce the fuzzy rules number dramatically. At last, some conclusions and discussion on further work are also given.

关键词： Takagi-Sugeno model Fuzzy systems Space technology Laboratories Polynomials Fuzzy sets intelligent systems Information science Computer science Fuzzy reasoning

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Task Scheduling for Heterogeneous Computing Based on Learning Classifier System

Task Scheduling for Heterogeneous Computing Based on Learnin...

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International Conference on Artificial Intelligence and Computational Intelligence (AICI)

作者： Jiadong Yang Hua Xu Peifa Jia State Key Laboratory Intelligent Technology and Systems Tsinghua National Laboratory of Information Science and Technology Department of Computer Science and Technology Tsinghua University Beijing China

Task scheduling still remains one of the most challenging problems to achieve high performance in heterogeneous computing environments in spite of numerous efforts. This paper presents a novel scheduling algorithm based on learning classifier system for heterogeneous computing environment. In the presented algorithm, XCS classifier system is used to find the optimal task assignment on different processors, and the execution sequence of tasks on the same processor is set by the heuristic used in list scheduling approach. Empirical studies on benchmark task graphs show that the proposed algorithm is able to produce higher speedup compared with the related algorithms. Further experiments also indicate that the proposed algorithm maintains almost the same performance with different parameter settings.

关键词： Processor scheduling Scheduling algorithm High performance computing Learning Clustering algorithms Artificial intelligence Computational intelligence intelligent systems Computational and artificial intelligence Competitive intelligence

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Stock Price Forecasting by Combining News Mining and Time Series Analysis

Stock Price Forecasting by Combining News Mining and Time Se...

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IEEE WIC ACM International Conference on Web Intelligence (WI)

作者： Xiangyu Tang Chunyu Yang Jie Zhou State Key Laboratory on Intelligent Technology and Systems Tsinghua National Laboratory of Information Science and Technology (TNList) Department of Automation Tsinghua University Beijing China

Stock price forecasting has aroused great concern in research of economy, machine learning and other fields. Time series analysis methods are usually utilized to deal with this task. In this paper, we propose to combine news mining and time series analysis to forecast inter-day stock prices. News reports are automatically analyzed with text mining techniques, and then the mining results are used to improve the accuracy of time series analysis algorithms. The experimental result on a half year Chinese stock market data indicates that the proposed algorithm can help to improve the performance of normal time series analysis in stock price forecasting significantly. Moreover, the proposed algorithm also performs well in stock price trend forecasting.

关键词： Time series analysis Economic forecasting Text mining intelligent agent Data mining Algorithm design and analysis Feature extraction technology forecasting Stock markets Conferences

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Probabilistic Labeled Semi-supervised SVM

Probabilistic Labeled Semi-supervised SVM

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IEEE International Conference on Data Mining Workshops (ICDM Workshops)

作者： Mingjie Qian Feiping Nie Changshui Zhang State Key Laboratory of Intelligent Technology and Systems Tsinghua National Laboratory of Information Science and Technology(TNList) Department of Automation Tsinghua University Beijing China

Semi-supervised learning has been paid increasing attention and is widely used in many fields such as data mining, information retrieval and knowledge management as it can utilize both labeled and unlabeled data. Laplacian SVM (LapSVM) is a very classical method whose effectiveness has been validated by large number of experiments. However, LapSVM is sensitive to labeled data and it exposes to cubic computation complexity which limit its application in large scale scenario. In this paper, we propose a multi-class method called Probabilistic labeled Semi-supervised SVM (PLSVM) in which the optimal decision surface is taught by probabilistic labels of all the training data including the labeled and unlabeled data. Then we propose a kernel version dual coordinate descent method to efficiently solve the dual problems of our Probabilistic labeled Semi-supervised SVM and decrease its requirement of memory. Synthetic data and several benchmark real world datasets show that PLSVM is less sensitive to labeling and has better performance over traditional methods like SVM, LapSVM (LapSVM) and Transductive SVM (TSVM).

关键词： Support vector machines Semisupervised learning Data mining Information retrieval Knowledge management Laplace equations Large-scale systems Training data Kernel Labeling

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Two dimensional Maximum Margin Criterion

Two dimensional Maximum Margin Criterion

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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Quanquan Gu Jie Zhou State Key Laboratory on Intelligent Technology and Systems Tsinghua National Laboratory of Information Science and Technology(TNList) Department of Automation Tsinghua University Beijing China

Maximum Margin Criterion is a well-known method for feature extraction and dimensionality reduction. In this paper, we propose a novel feature extraction method, namely Two Dimensional Maximum Margin Criterion (2DMMC), specifically for matrix representation data, e.g. images. 2DMMC aims to find two orthogonal projection matrices to project the original matrices to a low dimensional matrix subspace, in which a sample is close to those in the same class but far from those in different classes. Both theoretical analysis and experiments on benchmark face recognition data sets illustrate that the proposed method is very effective and efficient.

关键词： Linear discriminant analysis Feature extraction Principal component analysis Covariance matrix Scattering Laboratories intelligent systems Information science Automation Face recognition

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