ESANN 2005: Bruges, Belgium

Clustering, quantization, and self-organization

• Alfred Ultsch, Lutz Herrmann:
  The architecture of emergent self-organizing maps to reduce projection errors. 1-6
  
• Yann Prudent, Abdel Ennaji:
  A new learning algorithm for incremental self-organizing maps. 7-12
  
• Michael Biehl, Anarta Ghosh, Barbara Hammer:
  The dynamics of Learning Vector Quantization. 13-18
  
• Kevin Doherty, Rod Adams, Neil Davey:
  TreeGNG - hierarchical topological clustering. 19-24
  

Dynamical and Numerical Aspects of Neural Computing

• Miguel A. Atencia Ruiz, Gonzalo Joya Caparrós, Francisco Sandoval Hernández:
  Two or three things that we (intend to) know about Hopfield and Tank networks. 25-36
  
• Nigel Crook, Wee Jin Goh, Mohammad Hawarat:
  The Nonlinear Dynamic State neuron. 37-42
  
• Jochen J. Steil:
  Stability of backpropagation-decorrelation efficient O(N) recurrent learning. 43-48
  
• Francisco R. Villatoro:
  Exponential stability of implicit Euler, discrete-time Hopfield neural networks. 49-54
  
• Marie Kratz, Miguel A. Atencia Ruiz, Gonzalo Joya Caparrós:
  Stochastic analysis of the Abe formulation of Hopfield networks. 55-60
  
• Franck Carenzi, Philippe Gorce, Yves Burnod, Marc A. Maier:
  Using generic neural networks in the control and prediction of grasp postures. 61-66
  
• Mark P. Joy:
  Exponential stability of stochastic, retarded neural netorks. 67-72
  

Learning I

• Jean-Pierre Mano, Pierre Glize:
  Organization properties of open networks of cooperative neuro-agents. 73-78
  
• Shuyuan Yang, Min Wang, Licheng Jiao:
  A ridgelet kernel regression model using genetic algorithm. 79-84
  
• Vanessa Gómez-Verdejo, Manuel Ortega-Moral, Jerónimo Arenas-García, Aníbal R. Figueiras-Vidal:
  Boosting by weighting boundary and erroneous samples. 85-90
  

Artificial Neural Networks and Prognosis in Medicine

• Leonardo Franco, José M. Jerez, Emilio Alba:
  Artificial neural networks and prognosis in medicine. Survival analysis in breast cancer patients. 91-102
  
• Rachel Bittern, Alfred Cuschieri, Sergey Dolgobrodov, Robin Marshall, Peter Moore, Robert Steele:
  An artificial neural network for analysing the survival of patients with colorectal cancer. 103-108
  
• Maysam F. Abbod, Jim W. F. Catto, Min-You Chen, Derek A. Linkens, Freddie C. Hamdy:
  Artificial intelligence techniques for the prediction of bladder cancer progression. 109-114
  
• Hui Liu, Ash Kshirsagar, Jessica Ku, Dolores Lamb, Craig Niederberger:
  Computational models of intracytoplasmic sperm injection prognosis. 115-120
  
• Alfredo Vellido, Paulo J. G. Lisboa, Dolores Vicente:
  Handling outliers and missing data in brain tumour clinical assessment using t-GTM. 121-126
  
• Mark P. Joy, Simon Jones:
  predicting bed demand in a hospital using neural networks and ARIMA models: a hybrid approach. 127-132
  
• Alfredo Vellido, Paulo J. G. Lisboa:
  Functional topographic mapping for robust handling of outliers in brain tumour data. 133-138
  
• Barbara Hammer, Andreas Rechtien, Marc Strickert, Thomas Villmann:
  Relevance learning for mental disease classification. 139-144
  
• Olga Valenzuela, Ignacio Rojas Ruiz, Fernando Rojas Ruiz, Luisa Marquez:
  Automatic classification of prostate cancer using pseudo-gaussian radial basis function neural network. 145-150
  

Learning II

• Nadir Farah, Tarek Khadir, Mokhtar Sellami:
  Artificial neural network fusion: Application to Arabic words recognition. 151-156
  
• Jie Ni, Qing Song:
  Adaptive Simultaneous Perturbation Based Pruning Algorithm for Neural Control Systems. 157-162
  
• Shigeo Abe:
  Modified backward feature selection by cross validation. 163-168
  
• Agathoklis Krimpenis, George-Christopher Vosniakos:
  Initialisation improvement in engineering feedforward ANN models. 169-174
  
• Manuel Ortega-Moral, Vanessa Gómez-Verdejo, Jerónimo Arenas-García, Aníbal R. Figueiras-Vidal:
  An On-line Fisher Discriminant. 175-180
  
• Nowicki Dimitri, Oleksiy Dekhtyarenko:
  Averaging on Riemannian manifolds and unsupervised learning using neural associative memory. 181-186
  
• Pornchai Khlaeo-om, Suwat Kuntanapreeda:
  A Stability Condition for Neural Network Control of Uncertain Systems. 187-192
  
• Bruno Azzerboni, Fabio La Foresta, Nadia Mammone, Francesco Carlo Morabito:
  A new approach based on wavelet-ICA algorithms for fetal electrocardiogram extraction. 193-198
  
• Catherine Aaron:
  Graph-based normalization. 199-204
  
• Joshua E. Menke, Tony R. Martinez:
  Domain expert approximation through oracle learning. 205-210
  
• Tony J. Dodd, Tun M. Ladoni:
  Generalised Cross Validation for Noise-Free Data. 211-216
  

Perceptrons and Multi-Layer Perceptrons

• Luís M. Silva, Joaquim Marques de Sá, Luís A. Alexandre:
  Neural network classification using Shannon's entropy. 217-222
  
• Joseph Rynkiewicz:
  Efficient estimation of multidimensional regression model with multilayer perceptron. 223-227
  
• Matthew Draper, Taskin Koçak:
  Performance of EMI based mine detection using back-propagation neural networks. 228-234
  
• Joaquim Marques de Sá, Carlos S. Felgueiras:
  Perceptron Learning with Discrete Weights. 235-240
  

Evolutionary and neural computation

• Christian Igel, Bernhard Sendhoff:
  Synergies between Evolutionary and Neural Computation. 241-252
  
• Arjun Chandra, Xin Yao:
  Evolutionary framework for the construction of diverse hybrid ensembles. 253-258
  
• Yohannes Kassahun, Gerald Sommer:
  Efficient reinforcement learning through Evolutionary Acquisition of Neural Topologies. 259-266
  
• John A. Bullinaria:
  Evolving neural networks: Is it really worth the effort? 267-272
  
• Lars Gräning, Yaochu Jin, Bernhard Sendhoff:
  Efficient evolutionary optimization using individual-based evolution control and neural networks: A comparative study. 273-278
  
• Alexander Gepperth, Stefan Roth:
  Applications of multi-objective structure optimization. 279-284
  

Independent Component Analysis

• Antti Honkela, Tomas Östman, Ricardo Vigário:
  Empirical evidence of the linear nature of magnetoencephalograms. 285-290
  
• Kun Zhang, Lai-Wan Chan:
  To apply score function difference based ICA algorithms to high-dimensional data. 291-296
  
• Silvia Chiappa, David Barber:
  generative independent component analysis for EEG classification. 297-302
  

Classification using non-standard metrics

• Barbara Hammer, Thomas Villmann:
  Classification using non-standard metrics. 303-316
  
• Luh Yen, Denis Vanvyve, Fabien Wouters, François Fouss, Michel Verleysen, Marco Saerens:
  clustering using a random walk based distance measure. 317-324
  
• Alexei Vinokourov, Andrei N. Soklakov, Craig Saunders:
  A probabilistic framework for mismatch and profile string kernels. 325-330
  
• Marc Strickert, Nese Sreenivasulu, Winfriede Weschke, Udo Seiffert, Thomas Villmann:
  Generalized Relevance LVQ with Correlation Measures for Biological Data. 331-338
  
• Damien François, Vincent Wertz, Michel Verleysen:
  Non-Euclidean metrics for similarity search in noisy datasets. 339-344
  
• Paul F. Evangelista, Piero P. Bonissone, Mark J. Embrechts, Boleslaw K. Szymanski:
  unsupervised fuzzy ensembles and their use in intrusion detection. 345-350
  
• Fabrice Rossi, Aïcha El Golli, Yves Lechevallier:
  Usage Guided Clustering of Web Pages with the Median Self Organizing Map. 351-356
  
• Mustapha Lebbah, Aymeric Chazottes, Fouad Badran, Sylvie Thiria:
  Mixed Topological Map. 357-362
  
• Andrew Carnell, Daniel Richardson:
  linear algebra for time series of spikes. 363-368
  

Learning III

• Katharina Tluk von Toschanowitz, Barbara Hammer, Helge Ritter:
  Relevance determination in reinforcement learning. 369-374
  
• Michael Bensch, Michael Schröder, Martin Bogdan, Wolfgang Rosenstiel:
  Feature selection for high-dimensional industrial data. 375-380
  
• Kary Främling:
  Adaptive robot learning in a non-stationary environment. 381-386
  
• Oleksiy Dekhtyarenko, Valery Tereshko, Colin Fyfe:
  Phase transition in sparse associative neural networks. 387-392
  
• Claude F. Touzet, Pierrick Demoulin, Borís Burle, Franck Vidal, Françoise Macar:
  Neuromimetic model of interval timing. 393-398
  
• Markus Hagenbuchner, Alessandro Sperduti, Ah Chung Tsoi:
  Contextual Processing of Graphs using Self-Organizing Maps. 399-404
  
• Gianluca Bontempi:
  Structural feature selection for wrapper methods. 405-410
  
• Thomas P. Trappenberg:
  Coverage-performance estimation for classification with ambiguous data. 411-416
  
• Min Wang, Shuyuan Yang, Shunjun Wu:
  UWB radar target identification based on linear RBFNN. 417-422
  

Biologically inspired models

• Michael Lawrence, Thomas P. Trappenberg, Alan Fine:
  A multi-modular associator network for simple temporal sequence learning and generation. 423-428
  
• Christopher Johansson, Martin Rehn, Anders Lansner:
  Attractor neural networks with patchy connectivity. 429-434
  
• David Verstraeten, Benjamin Schrauwen, Dirk Stroobandt:
  Isolated word recognition using a Liquid State Machine. 435-440
  
• Tan Shan, Licheng Jiao:
  new evidences for sparse coding strategy employed in visual neurons: from the image processing and nonlinear approximation viewpoint. 441-446
  

Kernel methods and the exponential family

• Stéphane Canu, Alexander J. Smola:
  Kernel methods and the exponential family. 447-454
  
• Karsten M. Borgwardt, Omri Guttman, S. V. N. Vishwanathan, Alexander J. Smola:
  Joint Regularization. 455-460
  
• Frédéric Desobry, Manuel Davy, William J. Fitzgerald:
  A Class of Kernels For Sets of Vectors. 461-466
  
• Nathalie Villa, Fabrice Rossi:
  Support Vector Machine For Functional Data Classification. 467-472
  
• Vincent Guigue, Alain Rakotomamonjy, Stéphane Canu:
  Translation invariant classification of non-stationary signals. 473-478
  

Applications

• Stefan Klinger, Jim Austin:
  Chemical similarity searching using a neural graph matcher. 479-484
  
• Liva Ralaivola, Lin Wu, Pierre Baldi:
  SVM and pattern-enriched common fate graphs for the game of go. 485-490
  
• José Hiroki Saito, Thiago Vieira de Carvalho, Marcelo Hirakuri, André Saunite, Alessandro Noriaki Ide, Sandra Abib:
  Using CMU PIE Human Face Database to a Convolutional Neural Network - Neocognitron. 491-496
  
• Manuel Graña, F. Xabier Albizuri, Alicia D'Anjou:
  Morphological memories for feature extraction in hyperspectral images. 497-502
  

Learning IV

• Nima Reyhani, Jin Hao, Yongnan Ji, Amaury Lendasse:
  Mutual information and gamma test for input selection. 503-508
  
• Antti Sorjamaa, Amaury Lendasse, Michel Verleysen:
  Pruned lazy learning models for time series prediction. 509-514
  
• Noelia Sánchez-Maroño, Amparo Alonso-Betanzos, Enrique Castillo:
  a new wrapper method for feature subset selection. 515-520
  
• Fernando Rojas Ruiz, Carlos García Puntonet, Ignacio Rojas Ruiz:
  Analysis of contrast functions in a genetic algorithm for post-nonlinear blind source separation. 521-526
  
• Kee Khoon Lee, Gavin C. Cawley, Michael W. Bevan:
  sparse Bayesian promoter based gene classification. 527-532
  
• Georg Pölzlbauer, Andreas Rauber, Michael Dittenbach:
  Graph projection techniques for Self-Organizing Maps. 533-538
  
• Georgina Stegmayer, Omar Chiotti, Giancarlo Orengo:
  A Neural Network that helps building a Nonlinear Dynamical model of a Power Amplifier. 539-544
  
• Hervé Guillaume, Nathalie Denquive, Philippe Tarroux:
  Contextual priming for artificial visual perception. 545-550
  
• Panagiotis Saragiotis, Bogdan Vrusias, Khurshid Ahmad:
  Learning to classify a collection of images and texts. 551-556
  
• Zhongwen Luo, Hongzhi Liu, Zhengping Yang, Xincai Wu:
  Self-Organizing Maps computing on Graphic Process Unit. 557-562
  
• Kirmene Marzouki, Takeshi Yamakawa:
  Novel Algorithm for Eliminating Folding Effect in Standard SOM. 563-570
  
• Djaffar Ould Abdeslam, Jean Mercklé, Patrice Wira:
  Adaline-based estimation of power harmonics. 571-576
  
• Xiangrong Zhang, Shuang Wang, Tan Shan, Licheng Jiao:
  Radar target recognition using SVMs with a wrapper feature selection driven by immune clonal algorithm. 577-582
  
• Boussa Sofiane, Marin Mattieu, LeFoll Frank, Faure Alain, Leboulanger Francois:
  Experimental validation of a synapse model by adding synaptic conductances to excitable endocrine cells in culture. 583-588
  
• Min Wang, Shuyuan Yang, Shunjun Wu:
  A neural network approach of ultra-wideband nearfield adaptive beamforming. 589-594
  
• Andrea Caponnetto, Lorenzo Rosasco, Francesca Odone, Alessandro Verri:
  Support vector algorithms as regularization networks. 595-600
  
• Karsten Kube, Andreas Herzog, Bernd Michaelis, Ayoub Al-Hamadi, Ana D. de Lima, Thomas B. Voigt:
  Spike-timing-dependent plasticity in 'small world' networks. 601-606
  

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