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18th International Conference on Pattern Recognition, ICPR 2006

作者： Quan, Zhong-Hua Xia, O-Lei Huang, De-Shuang Lyu, Michael R. Lok, Tat-Ming Intelligent Computing Lab. Hefei Institute of Intelligent Machines CAS Department of Automation University of Science and Technology of China Hefei Anhui 230031 China Intelligent Computing Lab. Hefei Institute of Intelligent Machines CAS Hefei Anhui 230031 China Computer Science and Engineering Dept. Chinese University of Hong Kong Information Engineering Dept. Chinese University of Hong Kong

ISBN: (纸本)0769525210

In this paper, an online signature verification scheme based on spectrum analysis and Mahalanobis decision is proposed. We firstly divided signatures to a number of frames with variable widths according to the characteristics of the time sequences, and then employed the Fast Fourier transformation(FFT) to extract the spectrum of signatures. The distance between the Fourier coefficient within the corresponding frames is computed, and the Mahalanobis decision making is employed. Experimentation demonstrates that spectrum analysis based on windows with variable widths is effective for online signature signals. © 2006 IEEE.

关键词： Electronic document identification systems

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REALIZING TARGET LANGUAGE GENERATION IN DATA-ORIENTED ENGLISH-CHINESE MACHINE TRANSLATION

REALIZING TARGET LANGUAGE GENERATION IN DATA-ORIENTED ENGLIS...

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2006 International Conference on Machine Learning and Cybernetics(IEEE第五届机器学习与控制论坛)

作者： TAO ZHANG RUI FENG YUE-JIE ZHANG School of Information Management & Engineering Shanghai University of Finance & Economics Shanghai Department of Computer Science & Engineering Shanghai Key Laboratory of Intelligent Information Pro

This paper presents a kind of target language generation mechanism in Data-Oriented English-Chinese Machine Translation. This mechanism applies the theory of Data-Oriented Parsing used in language analysis traditionally into target language generation equally. Through linearizing the result of source language analysis -- a parse tree, the final translation in target language is generated. To prove the efficiency of the proposed method, the knowledge source is constructed based on the real-world English corpus, and the other real-world English corpus is used as the test set. The experiment result shows that the quality of translation in target language is satisfactory.

关键词： Data-Oriented Parsing target language generation treebank fragment bank fragment-combination-form bank generation-fragment-combination-form

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RESEARCH ON DUAL PATTERN OF UNSUPERVISED AND SUPERVISED WORD SENSE DISAMBIGUATION

RESEARCH ON DUAL PATTERN OF UNSUPERVISED AND SUPERVISED WORD...

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2006 International Conference on Machine Learning and Cybernetics(IEEE第五届机器学习与控制论坛)

作者： YAO-FENG WANG ZHI-TING XU YUE-JIE ZHANG TAO ZHANG Department of Computer Science & Engineering Shanghai Key Laboratory of Intelligent Information Pro School of Information Management & Engineering Shanghai University of Finance & Economics Shanghai

As an important work in the field of Natural Language Processing, Word Sense Disambiguation (WSD) has been a research focus since 1950. The task of WSD is very difficult to solve, and most of modern algorithms fail to reach an ideal level. The processing for WSD is to determine the sense of a polysemous word within a specific context, which involves two steps -- determining all the senses for the polysemous word and selecting the appropriate sense among them. In this paper,a dual pattern of WSD based on supervised and unsupervised learning is proposed. Hence, WSD problem can be solved under different circumstances and conditions. Also, an adapted extended Lesk algorithm is established. The experiment results show that the whole quality of unsupervised and supervised WSD is satisfactory.

关键词： Word Sense Disambiguation Support Vector Machine WordNet unsupervised learning supervised learning

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Nonlinear blind source separation using hybrid neural networks

Nonlinear blind source separation using hybrid neural networ...

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3rd International Symposium on Neural Networks, ISNN 2006 - Advances in Neural Networks

作者： Zheng, Chun-Hou Huang, Zhi-Kai Lyu, Michael R. Lok, Tat-Ming Intelligent Computing Lab Institute of Intelligent Machines Chinese Academy of Sciences P.O.Box 1130 Hefei Anhui China Department of Automation University of Science and Technology of China Hong Kong China Computer Science and Engineering Dept. Chinese University of Hong Kong Hong Kong Hong Kong Information Engineering Dept. Chinese University of Hong Kong Shatin Hong Kong

ISBN: (纸本)354034439X

This paper proposes a novel algorithm based on minimizing mutual information for a special case of nonlinear blind source separation: post-nonlinear blind source separation. A network composed of a set of radial basis function (RBF) networks, a set of multilayer perceptron and a linear network is used as a demixing system to separate sources in post-nonlinear mixtures. The experimental results show that our proposed method is effective, and they also show that the local character of the RBF network's units allows a significant speedup in the training of the system. © Springer-Verlag Berlin Heidelberg 2006.

关键词： Blind source separation

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gTRICLUSTER: A more general and effective 3D clustering algorithm for gene-sample-time microarray data

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PAKDD 2006 Workshop on Data Mining for Biomedical Applications, BioDM 2006

作者： Jiang, Haoliang Zhou, Shuigeng Guan, Jihong Zheng, Ying Department of Computer Science and Engineering Fudan University 220 Handan Road Shanghai 200433 China Shanghai Key Lab. of Intelligent Information Processing Fudan University 220 Handan Road Shanghai 200433 China Dept. of Computer Sci. and Technol. Tongji University Shanghai 200092 China

ISBN: (纸本)3540331042

Clustering is an important technique in microarray data analysis, and mining three-dimensional (3D) clusters in gene-sample-time (simply GST) microarray data is emerging as a hot research topic in this area. A 3D cluster consists of a subset of genes that are coherent on a subset of samples along a segment of time series. This kind of coherent clusters may contain information for the users to identify useful phenotypes, potential genes related to these phenotypes and their expression rules. TRICLUSTER is the state-of-the-art 3D clustering algorithm for GST microarray data. In this paper, we propose a new algorithm to mine 3D clusters over GST microarray data. We term the new algorithm gTRICLUSTER because it is based on a more general 3D cluster model than the one that TRICLUSTER is based on. gTRICLUSTER can find more biologically meaningful coherent gene clusters than TRICLUSTER can do. It also outperforms TRICLUSTER in robustness to noise. Experimental results on a real-world microarray dataset validate the effectiveness of the proposed new algorithm. © Springer-Verlag Berlin Heidelberg 2006.

关键词： Database systems

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A new learning algorithm for function approximation incorporating a priori information into extreme learning machine

A new learning algorithm for function approximation incorpor...

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3rd International Symposium on Neural Networks, ISNN 2006 - Advances in Neural Networks

作者： Han, Fei Lok, Tat-Ming Lyu, Michael R. Intelligent Computing Lab. Hefei Institute of Intelligent Machines Chinese Academy of Sciences P.O. Box 1130 Hefei Anhui 230031 China Department of Automation University of Science and Technology of China Hefei 230027 China Information Engineering Dept. Chinese University of Hong Kong Shatin Hong Kong Computer Science and Engineering Dept. Chinese University of Hong Kong Shatin Hong Kong

ISBN: (纸本)354034439X

In this paper, a new algorithm for function approximation is proposed to obtain better generalization performance and faster convergent rate. The new algorithm incorporates the architectural constraints from a priori information of the function approximation problem into Extreme Learning Machine. On one hand, according to Taylor theorem, the activation functions of the hidden neurons in this algorithm are polynomial functions. On the other hand, Extreme Learning Machine is adopted which analytically determines the output weights of single-hidden layer FNN. In theory, the new algorithm tends to provide the best generalization at extremely fast learning speed. Finally, several experimental results are given to verify the efficiency and effectiveness of our proposed learning algorithm. © Springer-Verlag Berlin Heidelberg 2006.

关键词： Algorithms

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Gene selection based on mutual information for the classification of multi-class cancer

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International Conference on intelligent Computing, ICIC 2006

作者： Guo, Sheng-Bo Lyu, Michael R. Lok, Tat-Ming Department of Automation University of Science and Technology of China Hefei Anhui 230026 China Intelligent Computation Lab Hefei Institute of Intelligent Machines Chinese Academy of Sciences P.O. Box 1130 Hefei Anhui 230031 China Computer Science and Engineering Dept. Chinese University of Hong Kong Shatin Hong Kong Information Engineering Dept. Chinese University of Hong Kong Shatin Hong Kong

ISBN: (纸本)3540372776

With the development of mirocarray technology, microarray data are widely used in the diagnoses of cancer subtypes. However, people are still facing the complicated problem of accurate diagnosis of cancer subtypes. Building classifiers based on the selected key genes from microarray data is a promising approach for the development of microarray technology;yet the selection of non-redundant but relevant genes is complicated. The selected genes should be small enough to allow diagnosis even in regular laboratories and ideally identify genes involved in cancer-specific regulatory pathways. Instead of the traditional gene selection methods used for the classification of two categories of cancers, in the present paper, a novel gene selection algorithm based on mutual information is proposed for the classification of multi-class cancer using microarray data, and the selected key genes are fed into the classifier to classify the cancer subtypes. In our algorithm, mutual information is employed to select key genes related with class distinction. The application on the breast cancer data suggests that the present algorithm can identify the key genes to the BRCA1 mutations/BRCA2 mutations/the sporadic mutations class distinction since the result of our proposed algorithm is promising, because our method can perform the classification of the three types of breast cancer effectively and efficiently. And two more microarray datasets, leukemia and ovarian cancer data, are also employed to validate the performance of our method. The performances of these applications demonstrate the high quality of our method. Based on the present work, our method can be widely used to discriminate different cancer subtypes, which will contribute to the development of technology for the recovery of the cancer. © Springer-Verlag Berlin Heidelberg 2006.

关键词： Genetic engineering

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Support vector machine-based feature selection for classification of liver fibrosis grade in chronic hepatitis C

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Journal of Medical Systems 2006年第5期30卷 389-394页

作者： Jiang, Zheng Yamauchi, Kazunobu Yoshioka, Kentaro Aoki, Kazuma Kuroyanagi, Susumu Iwata, Akira Yang, Jun Wang, Kai Department of Medical Information and Management Science Graduate School of Medicine Nagoya University Showa-ku Nagoya 466-8550 65 Tsurumai-cho Japan Department of Hepato-Gastroenterology Graduate School of Medicine Nagoya University Nagoya 466-8550 Japan Iwata-Lab. Department of Electrical and Computer Engineering Nagoya Institute of Technology Nagoya Japan

Although liver biopsy is currently regarded as the gold standard for staging liver fibrosis in chronic hepatitis C, it is a costly invasive procedure and carries a small risk for complication. Our aim in this study was to construct a simple model to distinguish between patients with no or mild fibrosis (METAVIR F0-F1) versus those with clinically significant fibrosis (METAVIR F2-F4). We retrospectively studied 204 consecutive CHC patients. Thirty-four serum markers with age, gender, duration of infection were assessed to classify fibrosis with a classifier known as the support vector machine (SVM). The method of feature selection known as sequential forward floating selection (SFFS) was introduced before the performance of SVM. When four serum markers were extracted with SFFS-SVM, F2-F4 could be predicted accurately in 96%. Our study showed that application of this model could identify CHC patients with clinically significant fibrosis with a high degree of accuracy and may decrease the need for liver biopsy. © Springer science+Business Media, Inc. 2006.

关键词： Chronic hepatitis C Fibrosis staging Sequential forward floating selection Support vector machine

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Evolving Apollonian networks with small-world scale-free topologies

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Physical Review E 2006年第4期74卷 046105-046105页

作者： Zhongzhi Zhang Lili Rong Shuigeng Zhou Department of Computer Science and Engineering Fudan University Shanghai 200433 China Shanghai Key Lab of Intelligent Information Processing Fudan University Shanghai 200433 China Institute of Systems Engineering Dalian University of Technology Dalian 116023 Liaoning China

We propose two types of evolving networks: evolutionary Apollonian networks (EANs) and general deterministic Apollonian networks (GDANs), established by simple iteration algorithms. We investigate the two networks by both simulation and theoretical prediction. Analytical results show that both networks follow power-law degree distributions, with distribution exponents continuously tuned from 2 to 3. The accurate expression of clustering coefficient is also given for both networks. Moreover, the investigation of the average path length of EAN and the diameter of GDAN reveals that these two types of networks possess small-world feature. In addition, we study the collective synchronization behavior on some limitations of the EAN.

关键词： SYNCHRONIZED CHAOS COMPLEX NETWORKS SYSTEMS LASERS MODEL

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Distributed mining of partial periodic patterns in sequences

Distributed mining of partial periodic patterns in sequences

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作者： Hsiao, Han-Wen Tsai, Meng-Shu Tsai, Jeffrey J. P. Department of Computer Science and Information Engineering Asia University Taiwan Taiwan Institute of Information Technology Asia University Taiwan Taiwan Department of Computer Science University of Illinois Chicago United States Taichung Healthcare and Management University Department of Computer Science and Information Engineering Department of Biotechnology and Bioinformatics Department of Electrical Engineering and Computer Science University of Illinois Chicago Distributed Real-Time Intelligent Systems Laboratory

It has been an important task of discovering frequent fragments as particular patterns from large sequence databases generated from a variety of applications. In general, the patterns to be discovered may partially and asynchronously exist in sequences, and even contain gaps. In addition, it is necessary to collect the information regarding the locations and frequencies of the patterns. How to enumerate candidate patterns for evaluation without exponentially increasing the computation is another problem. In this paper, the modified periodicity transform is proposed to meet the requirements mentioned above. Also, a distributed computing framework is implemented to perform the mining task more efficiently. Both synthetic and biological sequences are utilized to examine the approach. The experimental results demonstrate the efficiency and effectiveness the system.

关键词： Data mining

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