ISNN 2006: Chengdu, China

Neurobiological Analysis

• Si Wu, Jianfeng Feng, Shun-ichi Amari:
  The Ideal Noisy Environment for Fast Neural Computation. 1-6
  
• Xu-Dong Wang, Jiang Hao, Mu-Ming Poo, Xiao-Hui Zhang:
  How Does a Neuron Perform Subtraction? Arithmetic Rules of Synaptic Integration of Excitation and Inhibition. 7-14
  
• Jun Liu, Jian Wu, Zhengguo Lou, Guang Li:
  Stochastic Resonance Enhancing Detectability of Weak Signal by Neuronal Networks Model for Receiver. 15-20
  
• Huafu Chen, Ling Zeng, Dezhong Yao, Qing Gao:
  A Gaussian Dynamic Convolution Models of the FMRI BOLD Response. 21-26
  
• Mingxiao Ding, Naigong Yu, Xiaogang Ruan:
  Cooperative Motor Learning Model for Cerebellar Control of Balance and Locomotion. 27-33
  
• Toshihiko Matsuka:
  A Model of Category Learning with Attention Augmented Simplistic Prototype Representation. 34-40
  
• Toshihiko Matsuka, Arieta Chouchourelou:
  On the Learning Algorithms of Descriptive Models of High-Order Human Cognition. 41-49
  
• Rubin Wang, Jing Yu, Zhikang Zhang:
  A Neural Model on Cognitive Process. 50-59
  

Theoretical Analysis

• Zongben Xu, Jianjun Wang, Deyu Meng:
  Approximation Bound of Mixture Networks in L_omega^p Spaces. 60-65
  
• Feng-jun Li, Zongben Xu:
  Integral Transform and Its Application to Neural Network Approximation. 66-71
  
• Chunmei Ding, Feilong Cao, Zongben Xu:
  The Essential Approximation Order for Neural Networks with Trigonometric Hidden Layer Units. 72-79
  
• Yong Fang, Tommy W. S. Chow:
  Wavelets Based Neural Network for Function Approximation. 80-85
  
• Alejandro Cruz Sandoval, Wen Yu:
  Passivity Analysis of Dynamic Neural Networks with Different Time-Scales. 86-92
  
• Zhiguo Yang, Daoyi Xu, Yumei Huang:
  Exponential Dissipativity of Non-autonomous Neural Networks with Distributed Delays and Reaction-Diffusion Terms. 93-99
  
• Min-Jae Kang, Ho-Chan Kim, Farrukh Aslam Khan, Wang-Cheol Song, Jacek M. Zurada:
  Convergence Analysis of Continuous-Time Neural Networks. 100-108
  
• Weirui Zhao, Huanshui Zhang:
  Global Convergence of Continuous-Time Recurrent Neural Networks with Delays. 109-114
  
• Xiaoxin Liao, Zhigang Zeng:
  Global Exponential Stability in Lagrange Sense of Continuous-Time Recurrent Neural Networks. 115-121
  
• Yi Shen, Meiqin Liu, Xiaodong Xu:
  Global Exponential Stability of Recurrent Neural Networks with Time-Varying Delay. 122-128
  
• Gang Wang, Huaguang Zhang, Chonghui Song:
  New Criteria of Global Exponential Stability for a Class of Generalized Neural Networks with Time-Varying Delays. 129-134
  
• Changyin Sun, Linfeng Li:
  Dynamics of General Neural Networks with Distributed Delays. 135-140
  
• Shuyong Li, Yumei Huang, Daoyi Xu:
  On Equilibrium and Stability of a Class of Neural Networks with Mixed Delays. 141-146
  
• Hanlin He, Xiaoxin Liao:
  Stability Analysis of Neutral Neural Networks with Time Delay. 147-152
  
• Jianlong Qiu, Jinde Cao:
  Global Asymptotical Stability in Neutral-Type Delayed Neural Networks with Reaction-Diffusion Terms. 153-158
  
• Wudai Liao, Zhongsheng Wang, Xiaoxin Liao:
  Almost Sure Exponential Stability on Interval Stochastic Neural Networks with Time-Varying Delays. 159-164
  
• Xuyang Lou, Baotong Cui:
  Stochastic Robust Stability of Markovian Jump Nonlinear Uncertain Neural Networks with Wiener Process. 165-171
  
• Li Xie, Tianming Liu, Guodong Lu, Jilin Liu, Stephen T. C. Wong:
  Stochastic Robust Stability Analysis for Markovian Jump Discrete-Time Delayed Neural Networks with Multiplicative Nonlinear Perturbations. 172-178
  
• Jun Xu, Daoying Pi, Yong-Yan Cao:
  Global Robust Stability of General Recurrent Neural Networks with Time-Varying Delays. 179-184
  
• Yongqing Yang:
  Robust Periodicity in Recurrent Neural Network with Time Delays and Impulses. 185-191
  
• Tianping Chen, Wenlian Lu:
  Global Asymptotical Stability of Cohen-Grossberg Neural Networks with Time-Varying and Distributed Delays. 192-197
  
• Ce Ji, Huaguang Zhang, Chonghui Song:
  LMI Approach to Robust Stability Analysis of Cohen-Grossberg Neural Networks with Multiple Delays. 198-203
  
• Tianping Chen, Lili Wang, Changlei Ren:
  Existence and Global Stability Analysis of Almost Periodic Solutions for Cohen- Grossberg Neural Networks. 204-210
  
• Li-qun Zhou, Guang-da Hu:
  A New Sufficient Condition on the Complete Stability of a Class Cellular Neural Networks. 211-216
  
• Weifan Zheng, Jiye Zhang, Weihua Zhang:
  Stability Analysis of Reaction-Diffusion Recurrent Cellular Neural Networks with Variable Time Delays. 217-223
  
• Wudai Liao, Yulin Xu, Xiaoxin Liao:
  Exponential Stability of Delayed Stochastic Cellular Neural Networks. 224-229
  
• Chaojin Fu, Boshan Chen:
  Global Exponential Stability of Cellular Neural Networks with Time-Varying Delays and Impulses. 230-235
  
• Jiye Zhang, Dianbo Ren, Weihua Zhang:
  Global Exponential Stability of Fuzzy Cellular Neural Networks with Variable Delays. 236-242
  
• Tingwen Huang, Marco Roque-Sol:
  Stability of Fuzzy Cellular Neural Networks with Impulses. 243-248
  
• Xiaoxin Liao, Fei Xu, Pei Yu:
  Absolute Stability of Hopfield Neural Network. 249-254
  
• Bingji Xu, Qun Wang:
  Robust Stability Analysis of Uncertain Hopfield Neural Networks with Markov Switching. 255-260
  
• Wei Zhu, Daoyi Xu:
  Asymptotic Stability of Second-Order Discrete-Time Hopfield Neural Networks with Variable Delays. 261-266
  
• Shengrui Zhang, Runnian Ma:
  Convergence Analysis of Discrete Delayed Hopfield Neural Networks. 267-272
  
• Minghui Jiang, Yi Shen, Xiaoxin Liao:
  An LMI-Based Approach to the Global Stability of Bidirectional Associative Memory Neural Networks with Variable Delay. 273-278
  
• Hui Wang, Xiaofeng Liao, Chuandong Li, Degang Yang:
  Existence of Periodic Solution of BAM Neural Network with Delay and Impulse. 279-284
  
• Min Xiao, Jinde Cao:
  On Control of Hopf Bifurcation in BAM Neural Network with Delayed Self-feedback. 285-290
  
• Hong Qu, Zhang Yi:
  Convergence and Periodicity of Solutions for a Class of Discrete-Time Recurrent Neural Network with Two Neurons. 291-296
  
• Wentong Liao, Linshan Wang:
  Existence and Global Attractability of Almost Periodic Solution for Competitive Neural Networks with Time-Varying Delays and Different Time Scales. 297-302
  
• Jin Zhou, Tianping Chen, Xiang Lan, Meichun Liu:
  Global Synchronization of Impulsive Coupled Delayed Neural Networks. 303-308
  
• Ping Li:
  Synchronization of a Class of Coupled Discrete Recurrent Neural Networks with Time Delay. 309-315
  
• Yan Huang, Xiao-Song Yang:
  Chaos and Bifurcation in a New Class of Simple Hopfield Neural Network. 316-321
  
• Hongwei Wang, Hong Gu:
  Synchronization of Chaotic System with the Perturbation Via Orthogonal Function Neural Network. 322-327
  
• Haigeng Luo, Xiaodong Xu, Xiaoxin Liao:
  Numerical Analysis of a Chaotic Delay Recurrent Neural Network with Four Neurons. 328-333
  
• Guang-Hong Wang, Ping Jiang:
  Autapse Modulated Bursting. 334-343
  

Neurodynamic Optimization

• Guocheng Li, Shiji Song, Cheng Wu, Zifang Du:
  A Neural Network Model for Non-smooth Optimization over a Compact Convex Subset. 344-349
  
• Shiji Song, Guocheng Li, Xiaohong Guan:
  Differential Inclusions-Based Neural Networks for Nonsmooth Convex Optimization on a Closed Convex Subset. 350-358
  
• Fuye Feng, Yong Xia, Quanju Zhang:
  A Recurrent Neural Network for Linear Fractional Programming with Bound Constraints. 359-368
  
• Qingshan Liu, Jun Wang, Jinde Cao:
  A Delayed Lagrangian Network for Solving Quadratic Programming Problems with Equality Constraints. 369-378
  
• Yao-qun Xu, Ming Sun, Guangren Duan:
  Wavelet Chaotic Neural Networks and Their Application to Optimization Problems. 379-384
  
• Ling Qin, Yixin Chen, Ling Chen, Yuan Yao:
  A New Optimization Algorithm Based on Ant Colony System with Density Control Strategy. 385-390
  
• Paulo Henrique Siqueira, Sérgio Scheer, Maria Teresinha Arns Steiner:
  A New Neural Network Approach to the Traveling Salesman Problem. 391-398
  
• Lijun Liu, Wei Wu:
  Dynamical System for Computing Largest Generalized Eigenvalue. 399-404
  
• Yiguang Liu, Zhisheng You:
  A Concise Functional Neural Network for Computing the Extremum Eigenpairs of Real Symmetric Matrices. 405-413
  

Learning Algorithms

• Frank Emmert-Streib:
  A Novel Stochastic Learning Rule for Neural Networks. 414-423
  
• Deepak Mishra, Abhishek Yadav, Prem Kumar Kalra:
  Learning with Single Quadratic Integrate-and-Fire Neuron. 424-429
  
• Hongyu Li, I-Fan Shen:
  Manifold Learning of Vector Fields. 430-435
  
• Hongyu Li, I-Fan Shen:
  Similarity Measure for Vector Field Learning. 436-441
  
• Jinwen Ma, Bin Cao:
  The Mahalanobis Distance Based Rival Penalized Competitive Learning Algorithm. 442-447
  
• Seongwon Cho, Jaemin Kim, Sun-Tae Chung:
  Dynamic Competitive Learning. 448-455
  
• Jinwuk Seok, Seongwon Cho, Jaemin Kim:
  Hyperbolic Quotient Feature Map for Competitive Learning Neural Networks. 456-463
  
• Zhiwu Lu, Jinwen Ma:
  A Gradient Entropy Regularized Likelihood Learning Algorithm on Gaussian Mixture with Automatic Model Selection. 464-469
  
• Ah-Hwee Tan:
  Self-organizing Neural Architecture for Reinforcement Learning. 470-475
  
• SeungGwan Lee:
  On the Efficient Implementation Biologic Reinforcement Learning Using Eligibility Traces. 476-481
  
• Lianwei Zhao, Siwei Luo, Mei Tian, Chao Shao, Hongliang Ma:
  Combining Label Information and Neighborhood Graph for Semi-supervised Learning. 482-488
  
• Liang Liu, Naigong Yu, Mingxiao Ding, Xiaogang Ruan:
  A Cerebellar Feedback Error Learning Scheme Based on Kalman Estimator for Tracing in Dynamic System. 489-495
  
• Lei Guo, Hong Wang:
  An Optimal Iterative Learning Scheme for Dynamic Neural Network Modelling. 496-501
  
• Toshinori Deguchi, Naohiro Ishii:
  Delayed Learning on Internal Memory Network and Organizing Internal States. 502-508
  
• Huawei Chen, Fan Jin:
  A Novel Learning Algorithm for Feedforward Neural Networks. 509-514
  
• He-Sheng Tang, Song-Tao Xue, Rong Chen:
  On H_infinity Filtering in Feedforward Neural Networks Training and Pruning. 515-523
  
• Jinhua Xu, Daniel W. C. Ho:
  A Node Pruning Algorithm Based on Optimal Brain Surgeon for Feedforward Neural Networks. 524-529
  
• Eu Jin Teoh, Cheng Xiang, Kay Chen Tan:
  A Fast Learning Algorithm Based on Layered Hessian Approximations and the Pseudoinverse. 530-536
  
• Hai Zhao, Bao-Liang Lu:
  A Modular Reduction Method for k-NN Algorithm with Self-recombination Learning. 537-544
  
• Haixia Chen, Senmiao Yuan, Kai Jiang:
  Selective Neural Network Ensemble Based on Clustering. 545-550
  
• Songsong Li, Toshimi Okada, Xiaoming Chen, Zheng Tang:
  An Individual Adaptive Gain Parameter Backpropagation Algorithm for Complex-Valued Neural Networks. 551-557
  
• Marco A. Moreno-Armendariz, Giovanni Egidio Pazienza, Wen Yu:
  Training Cellular Neural Networks with Stable Learning Algorithm. 558-563
  
• Yangmin Li, Xin Chen:
  A New Stochastic PSO Technique for Neural Network Training. 564-569
  
• Ben Niu, Yunlong Zhu, Xiaoxian He:
  A Multi-population Cooperative Particle Swarm Optimizer for Neural Network Training. 570-576
  
• Haichang Gao, Boqin Feng, Yun Hou, Li Zhu:
  Training RBF Neural Network with Hybrid Particle Swarm Optimization. 577-583
  
• Ming-Jung Seow, Vijayan K. Asari:
  Robust Learning by Self-organization of Nonlinear Lines of Attractions. 584-589
  
• Kai Zhang, Gen-Zhi Guan, Fang-Fang Chen, Lin Zhang, Zhi-Ye Du:
  Improved Learning Algorithm Based on Generalized SOM for Dynamic Non-linear System. 590-598
  
• Kao-Shing Hwang, Yu-Jen Chen, Tzung-Feng Lin:
  Q-Learning with FCMAC in Multi-agent Cooperation. 599-606
  
• Xuesong Wang, Yuhu Cheng, Wei Sun:
  Q Learning Based on Self-organizing Fuzzy Radial Basis Function Network. 607-615
  
• Haisheng Lin, Xiao Zhi Gao, Xianlin Huang, Zhuoyue Song:
  A Fuzzy Neural Networks with Structure Learning. 616-622
  
• Mariela Cerrada, Jose Aguilar, André Titli:
  Reinforcement Learning-Based Tuning Algorithm Applied to Fuzzy Identification. 623-630
  
• Fei Han, Tat-Ming Lok, Michael R. Lyu:
  A New Learning Algorithm for Function Approximation Incorporating A Priori Information into Extreme Learning Machine. 631-636
  
• Jun-Seok Lim, Koeng-Mo Sung, Joonil Song:
  Robust Recursive Complex Extreme Learning Machine Algorithm for Finite Numerical Precision. 637-643
  
• You Xu, Yang Shu:
  Evolutionary Extreme Learning Machine - Based on Particle Swarm Optimization. 644-652
  
• You Xu:
  A Gradient-Based ELM Algorithm in Regressing Multi-variable Functions. 653-658
  
• Jaehun Lee, Wooyong Chung, Euntai Kim:
  A New Genetic Approach to Structure Learning of Bayesian Networks. 659-668
  

Model Design

• Shoujue Wang, Singsing Liu, Wenming Cao:
  Research on Multi-Degree-of-Freedom Neurons with Weighted Graphs. 669-675
  
• Hong Yue, Aurelie J. A. Leprand, Hong Wang:
  Output PDF Shaping of Singular Weights System: Monotonical Performance Design. 676-682
  
• Yi Shen, Meiqin Liu, Xiaodong Xu:
  Stochastic Time-Varying Competitive Neural Network Systems. 683-688
  
• Dong-Chul Park, Duc-Hoai Nguyen, Song-Jae Lee, Yunsik Lee:
  Heterogeneous Centroid Neural Networks. 689-694
  
• Shuzhong Yang, Siwei Luo, Jianyu Li:
  Building Multi-layer Small World Neural Network. 695-700
  
• Stones Lei Zhang, Zhang Yi, Jiancheng Lv:
  Growing Hierarchical Principal Components Analysis Self-Organizing Map. 701-706
  
• Andrey Gavrilov, Young-Koo Lee, Sungyoung Lee:
  Hybrid Neural Network Model Based on Multi-layer Perceptron and Adaptive Resonance Theory. 707-713
  
• Yan-Peng Liu, Ming-Guang Wu, Ji-Xin Qian:
  Evolving Neural Networks Using the Hybrid of Ant Colony Optimization and BP Algorithms. 714-722
  
• Dong-Sun Kim, Hyunsik Kim, Duck-Jin Chung:
  A Genetic Algorithm with Modified Tournament Selection and Efficient Deterministic Mutation for Evolving Neural Network. 723-731
  
• Yunhui Liu, Siwei Luo, Ziang Lv, Hua Huang:
  A Neural Network Structure Evolution Algorithm Based on e, m Projections and Model Selection Criterion. 732-738
  
• Zhuhong Zhang, Xin Tu, Chang-Gen Peng:
  A Parallel Coevolutionary Immune Neural Network and Its Application to Signal Simulation. 739-746
  
• Ji-Xiang Du, Chuan-Min Zhai, Zengfu Wang, Guo-Jun Zhang:
  A Novel Elliptical Basis Function Neural Networks Optimized by Particle Swarm Optimization. 747-751
  
• Ming Ma, Libiao Zhang, Jie Ma, Chunguang Zhou:
  Fuzzy Neural Network Optimization by a Particle Swarm Optimization Algorithm. 752-761
  
• Sumitra Mukhopadhyay, Ajit K. Mandal:
  Fuzzy Rule Extraction Using Robust Particle Swarm Optimization. 762-767
  
• Seok-Beom Roh, Sung-Kwun Oh, Tae-Chon Ahn:
  A New Design Methodology of Fuzzy Set-Based Polynomial Neural Networks with Symbolic Gene Type Genetic Algorithms. 768-773
  
• Sung-Kwun Oh, In-Tae Lee, Jeoung-Nae Choi:
  Design of Fuzzy Polynomial Neural Networks with the Aid of Genetic Fuzzy Granulation and Its Application to Multi-variable Process System. 774-779
  
• Ho-Sung Park, Sung-Kwun Oh, Tae-Chon Ahn:
  A Novel Self-Organizing Fuzzy Polynomial Neural Networks with Evolutionary FPNs: Design and Analysis. 780-785
  
• Sung-Kwun Oh, Byoung-Jun Park, Witold Pedrycz:
  Design of Fuzzy Neural Networks Based on Genetic Fuzzy Granulation and Regression Polynomial Fuzzy Inference. 786-791
  
• Lei Zhang, Guoyou Wang, Wentao Wang:
  A New Fuzzy ART Neural Network Based on Dual Competition and Resonance Technique. 792-797
  
• Chang-Wook Han, Jung-Il Park:
  Simulated Annealing Based Learning Approach for the Design of Cascade Architectures of Fuzzy Neural Networks. 798-803
  
• Huaguang Zhang, Yanhong Luo, Derong Liu:
  A New Fuzzy Identification Method Based on Adaptive Critic Designs. 804-809
  
• Wei-Hong Xu, Guo-Ping Chen, Zhong-Ke Xie:
  Impacts of Perturbations of Training Patterns on Two Fuzzy Associative Memories Based on T-Norms. 810-817
  
• Cornelio Yáñez-Márquez, Luis Pastor Sánchez Fernández, Itzamá López-Yáñez:
  Alpha-Beta Associative Memories for Gray Level Patterns. 818-823
  
• Zhigang Zeng, Jun Wang:
  Associative Memories Based on Discrete-Time Cellular Neural Networks with One-Dimensional Space-Invariant Templates. 824-829
  
• Roy Kwang Yang Chang, Chu Kiong Loo, Machavaram Venkata Chalapathy Rao:
  Autonomous and Deterministic Probabilistic Neural Network Using Global k-Means. 830-836
  
• Marija Bacauskiene, Vladas Cibulskis, Antanas Verikas:
  Selecting Variables for Neural Network Committees. 837-842
  
• Qingyu Xiong, Jian Huang, Xiaodong Xian, Qian Xiao:
  An Adaptive Network Topology for Classification. 843-848
  
• Emad A. M. Andrews Shenouda:
  A Quantitative Comparison of Different MLP Activation Functions in Classification. 849-857
  
• Eu Jin Teoh, Cheng Xiang, Kay Chen Tan:
  Estimating the Number of Hidden Neurons in a Feedforward Network Using the Singular Value Decomposition. 858-865
  
• Jiang Zhong, Chunxiao Ye, Yong Feng, Ying Zhou, Zhongfu Wu:
  Neuron Selection for RBF Neural Network Classifier Based on Multiple Granularities Immune Network. 866-872
  
• Yuehui Chen, Lizhi Peng, Ajith Abraham:
  Hierarchical Radial Basis Function Neural Networks for Classification Problems. 873-879
  
• Fang Liu, Jian-Zhong Zhou, Fangpeng Qiu, Jun-Jie Yang:
  Biased Wavelet Neural Network and Its Application to Streamflow Forecast. 880-888
  
• Yanlai Li, Kuanquan Wang, Tao Li:
  A Goal Programming Based Approach for Hidden Targets in Layer-by-Layer Algorithm of Multilayer Perceptron Classifiers. 889-894
  
• Zhou Yang, Wenjie Zhu, Liang Ji:
  SLIT: Designing Complexity Penalty for Classification and Regression Trees Using the SRM Principle. 895-902
  
• Haijun Li, Zheng-Xuan Wang, Limin Wang, Senmiao Yuan:
  Flexible Neural Tree for Pattern Recognition. 903-908
  
• Lei Wang, Yinling Nie, Weike Nie, Licheng Jiao:
  A Novel Model of Artificial Immune Network and Simulations on Its Dynamics. 909-914
  

Kernel Methods

• Bo Chen, Hongwei Liu, Zheng Bao:
  A Kernel Optimization Method Based on the Localized Kernel Fisher Criterion. 915-921
  
• Bo Jin, Yan-Qing Zhang:
  Genetic Granular Kernel Methods for Cyclooxygenase-2 Inhibitor Activity Comparison. 922-927
  
• Luoqing Li, Chenggao Wan:
  Support Vector Machines with Beta-Mixing Input Sequences. 928-935
  
• Fangfang Wu, Yinliang Zhao:
  Least Squares Support Vector Machine on Gaussian Wavelet Kernel Function Set. 936-941
  
• Huihong Jin, Zhiqing Meng, Xuanxi Ning:
  A Smoothing Multiple Support Vector Machine Model. 942-948
  
• Hongbing Liu, Shengwu Xiong, Xiaoxiao Niu:
  Fuzzy Support Vector Machines Based on Spherical Regions. 949-954
  
• Marek Bundzel, Tomás Kasanický, Baltazár Frankovic:
  Building Support Vector Machine Alternative Using Algorithms of Computational Geometry. 955-961
  
• Shengfeng Tian, Shaomin Mu, Chuanhuan Yin:
  Cooperative Clustering for Training SVMs. 962-967
  
• Xiaohong Wang, Sitao Wu, Xiaoru Wang, Qunzhan Li:
  SVMV - A Novel Algorithm for the Visualization of SVM Classification Results. 968-973
  
• Genting Yan, Guangfu Ma, Liangkuan Zhu:
  Support Vector Machines Ensemble Based on Fuzzy Integral for Classification. 974-980
  
• Shu Yu, Xiaowei Yang, Zhifeng Hao, Yanchun Liang:
  An Adaptive Support Vector Machine Learning Algorithm for Large Classification Problem. 981-990
  
• Woo-Sung Kang, Ki Hong Im, Jin Young Choi:
  SVDD-Based Method for Fast Training of Multi-class Support Vector Classifier. 991-996
  
• Bo Liu, Xiaowei Yang, Zhifeng Hao:
  Binary Tree Support Vector Machine Based on Kernel Fisher Discriminant for Multi-classification. 997-1003
  
• Jianhua Xu:
  A Fast and Sparse Implementation of Multiclass Kernel Perceptron Algorithm. 1004-1009
  
• Jingqing Jiang, Chuyi Song, Chunguo Wu, Yangchun Liang, Xiaowei Yang, Zhifeng Hao:
  Mutual Conversion of Regression and Classification Based on Least Squares Support Vector Machines. 1010-1015
  
• Liangzhi Gan, Hai-kuan Liu, Youxian Sun:
  Sparse Least Squares Support Vector Machine for Function Estimation. 1016-1021
  
• Feng-Qing Han, Da-Cheng Wang, Chuan-Dong Li, Xiao-Feng Liao:
  A Multiresolution Wavelet Kernel for Support Vector Regression. 1022-1029
  
• Zhen Yang, Jun Guo, Weiran Xu, Xiangfei Nie, Jian Wang, Jianjun Lei:
  Multi-scale Support Vector Machine for Regression Estimation. 1030-1037
  
• Dong-Chul Park, Chung-Nguyen Tran, Sancho Park:
  Gradient Based Fuzzy C-Means Algorithm with a Mercer Kernel. 1038-1043
  
• Yun-Wei Pu, Ming Zhu, Weidong Jin, Laizhao Hu:
  An Efficient Similarity-Based Validity Index for Kernel Clustering Algorithm. 1044-1049
  
• En-Hui Zheng, Min Yang, Ping Li, Zhi-Huan Song:
  Fuzzy Support Vector Clustering. 1050-1056
  
• Chengbo Wang, Chengan Guo:
  An SVM Classification Algorithm with Error Correction Ability Applied to Face Recognition. 1057-1062
  
• Zejian Yuan, Lei Yang, Yanyun Qu, Yuehu Liu, Xinchun Jia:
  A Boosting SVM Chain Learning for Visual Information Retrieval. 1063-1069
  
• Bo Wu, Liangpei Zhang, Pingxiang Li, Jinmu Zhang:
  Nonlinear Estimation of Hyperspectral Mixture Pixel Proportion Based on Kernel Orthogonal Subspace Projection. 1070-1075
  
• Li Sun, Ling Jing, Xiaodong Xia:
  A New Proximal Support Vector Machine for Semi-supervised Classification. 1076-1082
  
• Liefeng Bo, Ling Wang, Licheng Jiao:
  Sparse Gaussian Processes Using Backward Elimination. 1083-1088
  
• Xunkai Wei, Yinghong Li, Yue Feng:
  Comparative Study of Extreme Learning Machine and Support Vector Machine. 1089-1095
  

ICA and BSS

• Woong Myung Kim, Chan-Ho Park, Hyon-Soo Lee:
  Multi-level Independent Component Analysis. 1096-1102
  
• Tao Yu, Huai-Zong Shao, Qi-Cong Peng:
  An ICA Learning Algorithm Utilizing Geodesic Approach. 1103-1108
  
• Gang Wang, Nini Rao, Zhi-Lin Zhang, Quanyi Mo, Pu Wang:
  An Extended Online Fast-ICA Algorithm. 1109-1114
  
• Shangming Yang:
  Gradient Algorithm for Nonnegative Independent Component Analysis. 1115-1120
  
• Fasong Wang, Hongwei Li, Rui Li, Shaoquan Yu:
  Unified Parametric and Non-parametric ICA Algorithm for Arbitrary Sources. 1121-1126
  
• Xiao-fei Shi, Ji-dong Suo, Chang Liu, Li Li:
  A Novel Kurtosis-Dependent Parameterized Independent Component Analysis Algorithm. 1127-1132
  
• Gang Wang, Xin Xu, Dewen Hu:
  Local Stability Analysis of Maximum Nongaussianity Estimation in Independent Component Analysis. 1133-1139
  
• Mao Ye, Xue Li, Chengfu Yang, Zengan Gao:
  Convergence Analysis of a Discrete-Time Single-Unit Gradient ICA Algorithm. 1140-1146
  
• Ji-Min Ye, Shun-Tian Lou, Hai-Hong Jin, Xian-Da Zhang:
  An Novel Algorithm for Blind Source Separation with Unknown Sources Number. 1147-1152
  
• Gaoming Huang, Luxi Yang, Zhenya He:
  Blind Source Separation Based on Generalized Variance. 1153-1158
  
• Junying Zhang, Hongyi Zhang, Le Wei, Yue Joseph Wang:
  Blind Source Separation with Pattern Expression NMF. 1159-1164
  
• Chun-Hou Zheng, Zhi-Kai Huang, Michael R. Lyu, Tat-Ming Lok:
  Nonlinear Blind Source Separation Using Hybrid Neural Networks. 1165-1170
  
• Xiaolu Li, Zhaoshui He:
  Identification of Mixing Matrix in Blind Source Separation. 1171-1176
  
• Wenqiang Guo, Tianshuang Qiu, Yuzhang Zhao, Daifeng Zha:
  Identification of Independent Components Based on Borel Measure for Under-Determined Mixtures. 1177-1182
  
• Ronghua Li, Ming Xiao:
  Estimation of Delays and Attenuations for Underdetermined BSS in Frequency Domain. 1183-1188
  
• Gaoming Huang, Yang Gao, Luxi Yang, Zhenya He:
  Application of Blind Source Separation to Five-Element Cross Array Passive Location. 1189-1194
  
• Hua Zhang, Dazhang Feng:
  Convolutive Blind Separation of Non-white Broadband Signals Based on a Double-Iteration Method. 1195-1201
  
• Bin Xia, Liqing Zhang:
  Multichannel Blind Deconvolution Using a Novel Filter Decomposition Method. 1202-1207
  
• Feng Jiang, Liqing Zhang, Bin Xia:
  Two-Stage Blind Deconvolution for V-BLAST OFDM System. 1208-1213
  

Data Preprocessing

• Xuelei Hu, Lei Xu:
  A Comparative Study on Selection of Cluster Number and Local Subspace Dimension in the Mixture PCA Models. 1214-1221
  
• Ping Ling, Chunguang Zhou:
  Adaptive Support Vector Clustering for Multi-relational Data Mining. 1222-1230
  
• XuLei Yang, Qing Song, Meng Joo Er:
  Robust Data Clustering in Mercer Kernel-Induced Feature Space. 1231-1237
  
• Hyun-Chul Kim, Jaewook Lee:
  Pseudo-density Estimation for Clustering with Gaussian Processes. 1238-1243
  
• Lin Wang, Minghu Jiang, Yinghua Lu, Frank Noé, Jeremy C. Smith:
  Clustering Analysis of Competitive Learning Network for Molecular Data. 1244-1249
  
• Lin Wang, Minghu Jiang, Yinghua Lu, Frank Noé, Jeremy C. Smith:
  Self-Organizing Map Clustering Analysis for Molecular Data. 1250-1255
  
• Mao-ting Gao, Zheng-ou Wang:
  A Conscientious Rival Penalized Competitive Learning Text Clustering Algorithm. 1256-1260
  
• Kin Keung Lai, Lean Yu, Ligang Zhou, Shouyang Wang:
  Self-Organizing-Map-Based Metamodeling for Massive Text Data Exploration. 1261-1266
  
• Jiabing Wang, Hong Peng, Jing-Song Hu, Jun Zhang:
  Ensemble Learning for Keyphrases Extraction from Scientific Document. 1267-1272
  
• Yugang Fan, Ping Li, Zhi-Huan Song:
  Grid-Based Fuzzy Support Vector Data Description. 1273-1279
  
• Yang Weon Lee:
  Development of the Hopfield Neural Scheme for Data Association in Multi-target Tracking. 1280-1285
  
• Dong Sun, Yong Deng:
  Determine Discounting Coefficient in Data Fusion Based on Fuzzy ART Neural Network. 1286-1292
  
• Junlin Zhou, Yan Fu:
  Scientific Data Lossless Compression Using Fast Neural Network. 1293-1298
  
• Xiu-Rong Zhao, Qing He, Zhongzhi Shi:
  HyperSurface Classifiers Ensemble for High Dimensional Data Sets. 1299-1304
  
• Jen-Cheng Chen, Jia-Sheng Heh, Maiga Chang:
  Designing a Decompositional Rule Extraction Algorithm for Neural Networks. 1305-1311
  
• Qutang Cai, Changshui Zhang:
  Estimating Fractal Intrinsic Dimension from the Neighborhood. 1312-1318
  
• Junying Chen, Zheng Qin:
  Dimensionality Reduction for Evolving RBF Networks with Particle Swarms. 1319-1325
  
• Heyong Wang, Jie Zheng, Zheng-an Yao, Lei Li:
  Improved Locally Linear Embedding Through New Distance Computing. 1326-1333
  
• Dongyue Chen, LiMing Zhang:
  An Incremental Linear Discriminant Analysis Using Fixed Point Method. 1334-1339
  
• Jun-Seok Lim, Joonil Song, Yonggook Pyeon:
  A Prewhitening RLS Projection Alternated Subspace Tracking (PAST) Algorithm. 1340-1345
  
• Junping Zhang, Chao Shen, Jufu Feng:
  Classification with the Hybrid of Manifold Learning and Gabor Wavelet. 1346-1351
  
• Xizhao Wang, Hui Zhang:
  A Novel Input Stochastic Sensitivity Definition of Radial Basis Function Neural Networks and Its Application to Feature Selection. 1352-1358
  
• Mohammed Attik:
  Using Ensemble Feature Selection Approach in Selecting Subset with Relevant Features. 1359-1366
  
• Yan Wu, Yang Yang:
  A New Method for Feature Selection. 1367-1372
  
• Zongxia Xie, Qinghua Hu, Daren Yu:
  Improved Feature Selection Algorithm Based on SVM and Correlation. 1373-1380
  
• Ziqiang Wang, Dexian Zhang:
  Feature Selection in Text Classification Via SVM and LSI. 1381-1386
  
• Qijun Zhao, Hongtao Lu, David Zhang:
  Parsimonious Feature Extraction Based on Genetic Algorithms and Support Vector Machines. 1387-1393
  
• Hui Zhang, Tu Bao Ho, Mao Song Lin, Xuefeng Liang:
  Feature Extraction for Time Series Classification Using Discriminating Wavelet Coefficients. 1394-1399
  
• Gang Liu, Xi-Hai Li, Dai-Zhi Liu, Wei-Gang Zhai:
  Feature Extraction of Underground Nuclear Explosions Based on NMF and KNMF. 1400-1405
  
• Feng Zhu, Yafeng Hu, Xianda Zhang, Deguang Xie:
  Hidden Markov Model Networks for Multiaspect Discriminative Features Extraction from Radar Targets. 1406-1411
  
• Dong-hong Liu, Zhi-jie Chen, Wen-long Hu, Yong-shun Zhang:
  Application of Self-organizing Feature Neural Network for Target Feature Extraction. 1412-1420
  
• Shi-Fei Ding, Zhong-Zhi Shi:
  Divergence-Based Supervised Information Feature Compression Algorithm. 1421-1426
  

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