Clustering II

• 
  Yiu-ming Cheung, Lei Xu: A RPCL-Based Approach for Identification of Markov Model with Unknown Noise and Number of States. 3-8
• 
  Chi-Hyon Oh, Eriko Ikeda, Katsuhiro Honda, Hidetomo Ichihashi: Parameter Specification for Fuzzy Clustering by Q-Learning. 9-12
• 
  Magnus Rattray: A Model-Based Distance for Clustering. 13-16
• 
  André S. Henriques, Aluizio F. R. Araújo, Silvio Morato: Clustering Exploratory Activity in an Elevated Plus-Maze with Neural Networks. 17-22
• 
  Bruno Cernuschi-Frías, Enrique Carlos Segura: On Learning Mean Values in Hopfield Associative Memories Trained with Noisy Examples Using the Hebb Rule. 23-30

Special Session on 'Noisy Chaotic Neural Networks for Information Storage and Retrieval'

• 
  Paul J. Werbos: Extending Chaos and Complexity Theory to Address Life, Brain and Quantum Foundations. 31-36
• 
  Lipo Wang, Fuyu Tian: Noisy Chaotic Neural Networks for Solving Combinatorial Optimization Problems. 37-40
• 
  Frank C. Hoppensteadt, Eugene M. Izhikevich: Neural Computations by Networks of Oscillators. 41-46

Hardware Implementation V

• 
  Chun Lu, Bingxue Shi: Circuit Realization of a Programmable Neuron Transfer Function and Its Derivative. 47-50
• 
  Mario Costa, Davide Palmisano, Eros Pasero: NESP2: a Low Power Analog NEural Signal Processor with Analog Weight Storage. 51-56
• 
  Guoxing Li, Bingxue Shi: A Programmable Neural-Fuzzy Processor for Handwritten Digit Classification. 57-61
• 
  Teresa Serrano-Gotarredona, Bernabé Linares-Barranco, Andreas G. Andreou: Programmable Kernel Analog VLSI Convolution Chip for Real Time Vision Processing. 62-65
• 
  Gian Marco Bo, Daniele D. Caviglia, Maurizio Valle: An On-Chip Learning Neural Network. 66-74

Independent Component Analysis II

• 
  Michifumi Yoshioka, Sigeru Omatu: Independent Component Analysis Using Time Delayed Sampling. 75-78
• 
  Toshinao A. Kuzawa: Multiplicative Newton-Like Algorithm and Independent Component Analysis. 79-82
• 
  Aapo Hyvärinen, Patrik O. Hoyer, Mika Inki: Topographic ICA as a Model of V1 Receptive Fields. 83-88
• 
  Shiro Ikeda, Keisuke Toyama: ICA for Noisy Neurobiological Data. 89-96

Medical Applications IV

• 
  Thomas Waschulzik, Wilfried Brauer, T. Castedello, Bob Henery: Quality Assured Efficient Engineering of Feedforward Neural Networks with Supervised Learning (QUEEN) Evaluated with the 'Pima Indians Diabetes Database'. 97-102
• 
  Hiroshi Douzono, Shigeomi Hara, Yoshio Noguchi: A Clustering Method of Chromosome Fluorescence Profiles Using Modified Self Organizing Map Controlled by Simulated Annealing. 103-106
• 
  Zhen Zhang, Hong Zhang, Robert C. Bast Jr.: An Application of Artificial Neural Networks in Ovarian Cancer Early Detection. 107-112
• 
  Zheng Rong Yang, Robert G. Harrison, Weiping Lu: Identifying Health Inequalities Using Artificial Neural Networks (WHO Data). 113-118
• 
  Susan B. Garavaglia: Health Care Customer Satisfaction Survey Analysis Using Self-Organizing Maps and "Exponentially Smeared" Data Vectors. 119-126

Vision I

• 
  Dawei W. Dong: The Separability of Visual Sensitivity into Spatial Frequency and Motion Velocity Based on Properties of Natural Images. 127-132
• 
  Andrea Corradini, Horst-Michael Gross: Camera-Based Gesture Recognition for Robot Control. 133-138
• 
  Claudia Nölker, Helge Ritter: Parametrized SOMs for Hand Posture Reconstruction. 139-144
• 
  Monica Alderighi, Giacomo R. Sechi, Paolo Guazzoni, Stefania Russo, Luisa Zetta: A Pre-Attentive Neural System for the Analysis of Nuclear Physics Experimental Data. 145-152

Learnability

• 
  Andrzej Pacut: The Basic Diffusion Model Neuron Cannot Learn. 153-158
• 
  Bao-Liang Lu, Michinori Ichikawa: Emergence of Learning: An Approach to Coping with NP-Complete Problems in Learning. 159-164
• 
  Michael Schmitt: VC Dimension Bounds for Product Unit Networks. 165-170
• 
  Mitsunaga Kinjo, Shigeo Sato, Koji Nakajima: Characteristics of Small Scale Non-Monotonic Neuron Networks Having Large Potentiality for Learning. 171-176

Special Session on 'Multiclass Support Vector Machines'

• 
  Hélène Paugam-Moisy, André Elisseeff, Yann Guermeur: Generalization Performance of Multiclass Discriminant Models. 177-182
• 
  Yann Guermeur, André Elisseeff, Hélène Paugam-Moisy: A New Multi-Class SVM Based on a Uniform Convergence Result. 183-188
• 
  Chikahito Nakajima, Massimiliano Pontil, Tomaso Poggio: People Recognition and Pose Estimation in Image Sequences. 189-196

Hardware Implementation VI

• 
  Hossam Abdelbaki, Erol Gelenbe, Said E. El-Khamy: Analog Hardware Implementation of the Random Neural Network Model. 197-201
• 
  Tertulien Ndjountche, Rolf Unbehauen, Fa-Long Luo: A 1.5V VLSI Circuit for the Co-Channel Signal Separation. 202-207
• 
  Giacomo Indiveri: A 2D Neuromorphic VLSI Architecture for Modeling Selective Attention. 208-213
• 
  Yoondong Park, Jim-Shih Liaw, Theodore W. Berger, Bing J. Sheu: Compact VLSI Neural Network Circuit with High-Capacity Dynamic Synapses. 214-218
• 
  Francesco Diotalevi, Maurizio Valle, Gian Marco Bo, Daniele D. Caviglia: A VLSI Architecture for Weight Perturbation on Chip Learning Implementation. 219-226

Radial Basis Function Networks II

• 
  Yoshifusa Ito: Surface-Tracing Approximation by Basis Functions and Its Application to Neural Networks. 227-231
• 
  Ajit T. Dingankar, Dhananjay S. Phatak: Simultaneous Approximation with Neural Networks. 232-237
• 
  Michalis K. Titsias, Aristidis Likas: A Probabilistic RBF Network for Classification. 238-243
• 
  Lale Özyilmaz, Tülay Yildirim: Sensitivity Analysis for Conic Section Function Neural Networks. 244-252

Medical Applications V

• 
  Carlos Hernández-Espinosa, Mercedes Fernández-Redondo, Pedro Gómez Vilda: Diagnosis of Vocal and Voice Disorders by the Speech Signal. 253-258
• 
  Satu Tamminen, Susanna Pirttikangas, Juha Röning: Self-Organizing Maps in Adaptive Health Monitoring. 259-266

Motion Analysis

• 
  Erdogan Çesmeli, Delwin T. Lindsey, DeLiang L. Wang: An Oscillatory Correlation Model of Human Motion Perception. 267-272
• 
  Yuki Matsuzawa, Itsuo Kumazawa: Coarse-to-Fine Object Tracking Using a Shape Representation Network with Continuous Parameters Determining Shape Details. 273-278
• 
  Takashi Takahashi, Takio Kurita: Reconstructing Optical Flow Generated by Camera Rotation via Autoassociative Learning. 279-283
• 
  Homayoun Navabi: ARO: A Neural Network to Investigate a Time-Delayed Imposed Organization on First-Order and Second-Order Visual Stimuli at the Early Vision. 284-289
• 
  Felipe M. G. França, Zhijun Yang: Building Artificial CPGs with Asymmetric Hopfield Networks. 290-298

Non-Linear Control II

• 
  E. Irigoyen, José B. Galván, María José Pérez-Ilzarbe: Neural Networks for Constrained Optimal Control of Non-Linear Systems. 299-304
• 
  Bo Ling: Neural Network Based Feedforward Adapter for Batch Process Control. 305-310
• 
  Heidar A. Talebi, Rajnikant V. Patel, Khashayar Khorasani: A Neural Network Controller for a Discrete-Time Nonlinear Non-Minimum Phase System. 311-316
• 
  Mark A. Motter: Predictive Multiple Model Switching Control with the Self-Organizing Map. 317-324

Non-Linear Control III

• 
  Ruangrit Ekachaiworasin, Suwat Kuntanapreeda: A Training Rule Which Guarantees Finite-Region Stability of Neural Network Closed-Loop Control: An Extension to Nonhermitian Systems. 325-330
• 
  V. Paraskevopoulos, Chris R. Chatwin, Malcolm I. Heywood: Continuous Optimal Controllers Using Hierarchical Mixtures of Experts. 331-336
• 
  Cinvestav-Ipn: An Indirect Adaptive Neural Control of Nonlinear Plants. 337-344

Non-Linear Control IV

• 
  Toru Fujinaka, Yoshiyuki Kishida, Michifumi Yoshioka, Sigeru Omatu: Stabilization of Double Inverted Pendulum with Self-Tuning Neuro-PID. 345-348
• 
  Claudio Pernechele, Enrico Giro, Favio Bortoletto: Neural Network Algorithm Controlling a Hexapod Platform. 349-352
• 
  Elena I. Gaura, Richard J. Rider, Nigel Steele: Developing Smart Micromachined Transducers Using Feed-Forward Neural Networks: A System Identification and Control Perspective. 353-358
• 
  Chris Manzie, Marimuthu Palaniswami, H. Watson: Model Predictive Control of a Fuel Injection System with a Radial Basis Function Network Observer. 359-364
• 
  Miguel Damas, Moisés Salmerón, Julio Ortega: ANNs and GAs for Predictive Controlling of Water Supply Networks. 365-372

System Identification II

• 
  Ralf Kretzschmar, Hans Richner, Nicolaos B. Karayiannis: NEURO-BRA: A Bird Removal Approach for Wind Profiler Data Based on Quantum Neural Networks. 373-378
• 
  J. J. Vega, M. R. Reynoso, Miguel Arias-Estrada, Leopoldo Altamirano Robles: Bragg Curve Identification Using a Neural Network. 379-382
• 
  Eimei Oyama, Taro Maeda, Susumu Tachi: Goal-Directed Property of On-line Direct Inverse Modeling. 383-388
• 
  Marcelino Lázaro, Ignacio Santamaría, Carlos Pantaleón, C. Navarro, Antonio Tazón, T. Fernández: A Modular Neural Network for Global Modeling of Microwave Transistors. 389-394
• 
  Angel Mediavilla, Antonio Tazón, J. A. Pereda, Marcelino Lázaro, Ignacio Santamaría, Carlos Pantaleón: Neuronal Architecture for Waveguide Inductive Iris Bandpass Filter Optimization. 395-402

Time Series Prediction II

• 
  E. Chibaro, S. Fichera, Giovanni Muscato: Octane Number Prediction in a Reforming Plant. 403-407
• 
  Linyu Yang, Zhong He, John Yen, Ching Wu: A Neural Network Approach to Predict Existing and Infill Oil Well Performance. 408-413
• 
  Henrique S. Hippert, Carlos E. Pedreira, Reinaldo Castro Souza: Combining Neural Networks and ARIMA Models for Hourly Temperature Forecast. 414-419
• 
  A. R. Sadeghian, J. D. Lavers: Application of Feedfoward Neuro-Fuzzy Networks for Current Prediction in Electric Arc Furnaces. 420-428

Time Series Prediction III

• 
  Anto Satriyo Nugroho, Susumu Kuroyanagi, Akira Iwata: Fog Forecasting Using Self Growing Neural Network 'CombNET-II: ' A Solution for Imbalanced Training Sets Problem. 429-434
• 
  André Dantas, Koshi Yamamoto, Marcus V. Lamar, Yaeko Yamashita: Neural Network for Travel Demand Forecast Using GIS and Remote Sensing. 435-440
• 
  Anatoly Sachenko, Volodymyr Kochan, Volodymyr Turchenko, Vladimir A. Golovko, Yury Savitsky, A. Dunets, Theodore Laopoulos: Sensor Errors Prediction Using Neural Networks. 441-448

Time Series Prediction IV

• 
  Ignacio Rojas, Héctor Pomares, Jesús González, Eduardo Ros, Moisés Salmerón, Julio Ortega, Alberto Prieto: A New Radial Basis Function Networks Structure: Application to Time Series Prediction. 449-454
• 
  Lars U. Hjorth, Ian T. Nabney: Bayesian Training of Mixture Density Networks. 455-460
• 
  Zouhour Neji Ben Salem, Fériel Mouria-beji: Neural Network and Time Series Identification and Prediction. 461-466
• 
  Tomoo Aoyama, Hanxi Zhu, Ikuo Yoshihara: Forecasting of the Chaos by Iterations Including Multi-Layer Neural-Network. 467-471
• 
  Xiaohui Yu, Lei Xu: Adaptive Improved Portfolio Sharpe Ratio Maximization with Diversification. 472-478

Spiking Neural Networks III

• 
  Konrad P. Körding, Peter König: Two Sites of Synaptic Integration: Relevant for Learning? 479-484
• 
  Nicolas Langlois, Pierre Miché, Abdelaziz Bensrhair: Analogue Circuits of a Learning Spiking Neuron Model. 485-489
• 
  Tim Schönauer, Sahin Atasoy, Nasser Mehrtash, Heinrich Klar: Simulation of a Digital Neuro-Chip for Spiking Neural Networks. 490-498

Supervised Learning IV

• 
  Valentina Colla, Mirko Sgarbi, Leonardo Maria Reyneri: Neuro-Wavelet Parametric Modeling. 499-504
• 
  Yves Grandvalet: Bagging Down-Weights Leverage Points. 505-510
• 
  Katsuyuki Hagiwara, Kazuhiro Kuno: Regularization Learning and Early Stopping in Linear Networks. 511-516
• 
  Jés Jesus Fiais Cerqueira, Álvaro G. B. Palhares, Marconi K. Madrid: A Complement to the Back-Propagation Algorithm: An Upper Bound for the Learning Rate. 517-522
• 
  Weber Martins, Carlos Galvão Pinheiro Jr.: On-Line Expansion of Goal Seeking Neuron Networks. 523-528

Supervised Learning V

• 
  Herna L. Viktor: Generating New Patterns for Information Gain and Improved Neural Network Learning. 529-534
• 
  M. Winter, Giorgio Metta, Giulio Sandini: Neural-Gas for Function Approximation: A Heuristic for Minimizing the Local Estimation Error. 535-538
• 
  M. Winter, Giorgio Metta, Giulio Sandini: Adding Reinforcement Learning Features to the Neural-Gas Method. 539-542
• 
  Mercedes Fernández-Redondo, Carlos Hernández-Espinosa: A Comparison among Weight Initialization Methods for Multilayer Feedforward Networks. 543-548
• 
  Ah Chung Tsoi, Markus Hagenbuchner, Alessio Micheli: Building MLP Networks by Construction. 549-556

Recurrent Networks II

• 
  Felix A. Gers, Jürgen Schmidhuber: Neural Processing of Complex Continual Input Streams. 557-562
• 
  Pedro Henrique Gouvea Coelho: An Extended RTRL Training Algorithm Using Hessian Matrix. 563-568
• 
  F. Rosati, Paolo Campolucci, Francesco Piazza: A General Approach to Gradient Based Learning in Multirate Systems and Neural Networks. 569-576

Recurrent Networks III

• 
  Alex Aussem: Sufficient Conditions for Error Back Flow Convergence in Dynamical Recurrent Neural Networks. 577-582
• 
  Pu Sun, Kenneth A. Marko: Estimation of the Training Efficiency of Recurrent Neural Networks. 583-588
• 
  Josefina Barrera-Cortés, Ieroham S. Baruch: Recurrent Neural Network Model of a Fed-Batch Saccharomyces Cerevisiae Fermentation Process. 589-596

Unsupervised Learning II

• 
  Andrew Luk, Sandra Lien: Dynamics of the Generalized Lotto-Type Competitive Learning. 597-601
• 
  Masahiro Iwasaki, Shigeru Okuma, Tomonori Hashiyama: Self-Creating and Organizing Neural Networks with Weight Duplication. 602-607
• 
  Cornelius Weber, Klaus Obermayer: Structured Models from Structured Data: Emergence of Modular Information Processing within One Sheet of Neurons. 608-613
• 
  Pei Ling Lai, Colin Fyfe: Kernel and Nonlinear Canonical Correlation Analysis. 614

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