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2006 International Conference on Machine Learning and Cybernetics

作者： Wang, Chao-Yong Wu, Chun-Guo Liang, Yan-Chun Guo, Xin-Chen Key Laboratory of Symbol Computation and Knowledge Engineering Jilin University Ministry of Education Changchun 130012 China Department of Fundamental Sciences Jilin Teacher's Institute of Engineering and Technology Changchun 130021 China Key Laboratory of Information Science and Engineering Beijing Jiaotong University Railway Ministry Beijing 100044 China

ISBN: (纸本)1424400619

An efficient method for the diagnosis of breast cancer tumor is proposed based on Independent Component Analysis (ICA) and Least Square Support Vector Machine (LS-SVM). In order to save the expense of detection, firstly, variables are selected based on the theory of statistics. Then the ICA is introduced in a concise way and followed by extracting the ICA component from these selected variables. Finally the processed data are classified by the LS-SVM. Experimental and analytical results show that in the diagnosis of breast cancer tumor the proposed method is superior to the classical B-P algorithm. © 2006 IEEE.

关键词： Biomedical engineering

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Support vector machine regression algorithm based on chunking incremental learning

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ICCS 2006: 6th International Conference on computational Science

作者： Jingqing, Jiang Chuyi, Song Chunguo, Wu Maurizio, Marchese Yangchun, Liang Key Laboratory of Symbol Computation and Knowledge Engineering Jilin University Ministry of Education Changchun 130012 China College of Mathematics and Computer Science Inner Mongolia University for Nationalities Tongliao 028043 China Key Laboratory of Information Science and Engineering Beijing Jiaotong University Railway Ministry Beijing 100044 China Italy

ISBN: (纸本)3540343792

On the basis of least squares support vector machine regression (LSSVR), an adaptive and iterative support vector machine regression algorithm based on chunking incremental learning (CISVR) is presented in this paper. CISVR is an iterative algorithm and the samples are added to the working set in batches. The inverse of the matrix of coefficients from previous iteration is used to calculate the regression parameters. Therefore, the proposed approach permits to avoid the calculation of the inverse of a large-scale matrix and improves the learning speed of the algorithm. Support vectors are selected adaptively in the iteration to maintain the sparseness. Experimental results show that the learning speed of CISVR is improved greatly compared with LSSVR for the similar training accuracy. At the same time the number of the support vectors obtained by the presented algorithm is less than that obtained by LSSVR greatly. © Springer-Verlag Berlin Heidelberg 2006.

关键词： Learning systems

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A dynamic time delay neural network for ultrasonic motor identification and control

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

作者： Liang, Yanchun Zhang, Jie Xu, Xu Yang, Xiaowei Hao, Zhifeng Key Laboratory of Symbol Computation and Knowledge Engineering Jilin University Ministry of Education Changchun 130012 China College of Mathematics Jilin University Changchun 130012 China School of Mathematical Science South China University of Technology Guangzhou 510640 China

ISBN: (纸本)3540344373

A novel dynamic time delay neural network is proposed for ultrasonic motors identification and control in this paper. By introducing time delay neurons, the neural network identifier and controller of ultrasonic motors are constructed. Both of them are trained online by using an improved back-propagation algorithm. The usefulness and validity of the presented algorithm is examined by the experiments. © Springer-Verlag Berlin Heidelberg 2006.

关键词： Neural networks

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An Improved Artificial Immune Algorithm with a Dynamic Threshold

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Journal of Bionic Engineering 2006年第2期3卷 93-97页

作者： Zhang Qiao Xu Xu Liang Yan-chun Key Laboratory of Symbol Computation and Knowledge Engineering （Ministry of Education P. R. China） Qianwei Campus Jilin University Changchun 130012 P. R. China College of Mathematics Qianwei Campus Jilin University Changchun 130012 P. R. China

An improved artificial immune algorithm with a dynamic threshold is presented. The calculation for the affinity function in the real-valued coding artificial immune algorithm is modified through considering the antibody＇s fitness and setting the dynamic threshold value. Numerical experiments show that compared with the genetic algorithm and the originally real-valued coding artificial immune algorithm, the improved algorithm possesses high speed of convergence and good performance for preventing premature convergence.

关键词： dynamic threshold artificial immune algorithm genetic algorithm antibody

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Identification of Transcription Factor Binding Sites Using GA and PSO

Identification of Transcription Factor Binding Sites Using G...

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International Conference on Intelligent Systems Design and Applications (ISDA)

作者： Xiao-yu Chang Chun-guang Zhou Yan-wen Li Ping Hu College of Computer Science Key Laboratory of Symbol Computation and Knowledge Engineering of the Ministry of Education Jilin University China College of Computer Science Key Laboratory of Symbol Computation and Knowledge Engineering of the Ministry of Education department of computer science Northeast Normal University Changchun China

Identification of transcription factor binding sites from the upstream regions of genes is a highly important and unsolved problem. In this paper, we propose a novel framework for using evolutionary algorithm to solve this challenging issue. Under this framework, we use two prevalent evolutionary algorithms: genetic algorithm (GA) and particle swarm optimization (PSO) to find unknown sites in a collection of relatively long intergenic sequences that are suspected of being bound by the same factor. This paper represents binding sites motif to position weight matrix (PWM) and introduces how to code PWM to genome for GA and how to code it to particle for PSO. We apply these two algorithms to 5 different yeast saccharomyces cerevisiae transcription factor binding sites and CRP binding sites. The results on saccharomyces cerevisiae show that it can find the correct binding sites motifs, and the result on CRP shows that these two algorithms can achieve more accuracy than MEME and Gibbs sampler

关键词： Evolutionary computation Pulse width modulation Genomics Bioinformatics Genetic algorithms Particle swarm optimization Biology computing Evolution (biology) educational institutions Computer science

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Combining Greedy Method and Genetic Algorithm to Identify Transcription Factor Binding Sites

Combining Greedy Method and Genetic Algorithm to Identify Tr...

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International Conference on Hybrid Intelligent Systems (HIS)

作者： Wanyu Wang Xiaoyu Chang Chunguang Zhou College of Computer Science Key Laboratory of Symbol Computation and Knowledge Engineering of the Ministry of Education Jilin University Changchun China

Identification of Transcription Factor Binding Sites (TFBS) from the upstream region of genes remains a highly important and unsolved problem particularly in higher eukaryotic genomes. In this paper, we propose a novel approach to identify transcription factor binding sites. This approach combines greedy method and genetic algorithm (CGGA) to search conserved segment in the given sequence set. A new greedy method which can efficiently search a local optimal result is proposed. In order to solve the high complexity of this algorithm, we also give an effective improvement for this method. Then, we describe how to combine genetic algorithm with this greedy method to find the more optimal results. Greedy method is combined to the fitness function of the genetic algorithm. We apply this approach on two different TFBS sets and the results show that it can find correct result both effective and efficient, and for CRP binding sites, it get a more accurate result than Gibbs Sampler, AlignACE and MDGA.

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Prediction of Transcription Factor Binding Sites Using Genetic Algorithm

Prediction of Transcription Factor Binding Sites Using Genet...

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IEEE Conference on Industrial Electronics and Applications (ICIEA)

作者： Xiaoyu Chang Wengang Zhou Chunguang Zhou Yanchun Liang College of Computer Science and Technology Key Laboratory of Symbol Computation and Knowledge Engineering of the Ministry of Education Jilin University Changchun China

Identification of transcription factor binding sites (TFBS) from the upstream region of genes remains a highly important and unsolved problem particularly in higher eukaryotic genomes. In this paper, we propose a new approach to predict TFBS. This approach uses position weight matrix (PWM) to represent binding sites and uses genetic algorithm (GA) to search the best matrix. A new coding method so called multiple-variable coding is proposed in GA. We apply it on two transcription factors rebl and mgl. The result shows that this approach can find most of the known sites, which indicates that this method is very effective

关键词： Genetic algorithms Pulse width modulation Sequences Genomics Bioinformatics Hidden Markov models Proteins Computer science educational technology Knowledge engineering

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Mutual conversion of regression and classification based on least squares support vector machines

Mutual conversion of regression and classification based on ...

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

作者： Jiang, Jing-Qing Song, Chu-Yi Wu, Chun-Guo Liang, Yang-Chun Yang, Xiao-Wei Hao, Zhi-Feng Key Laboratory of Symbol Computation and Knowledge Engineering Jilin University Ministry of Education Changchun 130012 China College of Mathematics and Computer Science Inner Mongolia University for Nationalities Tongliao 028043 China Key Laboratory of Information Science and Engineering Beijing Jiaotong University Railway Ministry Beijing 100044 China School of Mathematical Science South China University of Technology Guangzhou 510640 China

ISBN: (纸本)354034439X

Classification and regression are most interesting problems in the fields of pattern recognition. The regression problem can be changed into binary classification problem and least squares support vector machine can be used to solve the classification problem. The optimal hyperplane is the regression function. In this paper, a one-step method is presented to deal with the multi-category problem. The proposed method converts the problem of classification into the function regression problem and is applied to solve the converted problem by least squares support vector machines. The novel method classifies the samples in all categories simultaneously only by solving a set of linear equations. Demonstrations of numerical experiments are performed and good performances are obtained. Simulation results show that the regression and classification can be converted each other based on least squares support vector machines. © Springer-Verlag Berlin Heidelberg 2006.

关键词： Learning systems

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Fuzzy kernel clustering based on particle swarm optimization

Fuzzy kernel clustering based on particle swarm optimization

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IEEE International Conference on Granular Computing (GRC)

作者： Libiao Zhang Chunguang Zhou Ming Ma Xiaohua Liu Chunxia Li Caitang Sun Miao Liu College of Computer Science and Technology Key Laboratory for Symbol Computation and Knowledge Engineering of the National Education Ministry of China Jilin University Changchun China

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Intelligent Sub-Object Image Retrieval System

Intelligent Sub-Object Image Retrieval System

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World Congress on Intelligent Control and Automation (WCICA)

作者： Wenhui Li Dongfeng Han Yi Wang Xiaosuo Lu Key Laboratory of Symbol Computation and Knowledge Engineering of the Ministry of Education College of Computer Science&Technology Jilin University Changchun Jilin China

This paper describes a sub-object retrieval system based on a segmentation method. We also use dynamic partial function (DPF) and indexing by locality sensitive hashing (LSH) for improving system performance. Such a system is useful for finding a sub-object from a large image database. In order to obtain the sub-object from a sample image, we use a segmentation method to cut out the object. The system utilizes the segmentation results to capture the higher-level concept of images and gets a stable and accurate result. Experimental and comparison results, which are performed using a general purpose database containing 20,000 images, are encouraging

关键词： Image retrieval Image segmentation Image databases Vehicle dynamics Indexing Information retrieval Content based retrieval Spatial databases Humans Laboratories

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