Artificial Neural Networks - ICANN 2010: 20th International Conference, Thessaloniki, Greece, Septmeber 15-18, 2010, Proceedings, Part II

Lecture Notes in Computer Science 6353......Page 1
Artificial Neural Networks – ICANN 2010: 20th International Conference / Thessaloniki, Greece, September 15-18, 2010 / Proceedings, Part II......Page 2
Preface......Page 4
Organization......Page 7
Table of Contents – Part II......Page 9
Introduction......Page 15
L2 Support Vector Regressors in the Dual Form......Page 16
Training Methods......Page 18
Convergence Improvement......Page 19
Performance Comparison......Page 20
Conclusions......Page 23
References......Page 24
Introduction......Page 25
Reproducing Kernel Hilbert Spaces......Page 27
Wirtinger's Calculus in Complex RKHS......Page 28
Complex Kernel LMS......Page 30
Experiments......Page 32
References......Page 33
Introduction......Page 35
Material and Methods......Page 36
ε-SV Regression (ε-SVR)......Page 37
Fuzzy Weighted SVR with Fuzzy Partition......Page 38
Application......Page 40
Results for the Data Group with Output in the Interval......Page 41
Conclusions......Page 42
References......Page 43
Introduction......Page 44
First and Second Order SMO......Page 45
Better Directions for Second Order SMO......Page 47
Numerical Experiments......Page 48
Discussion and Conclusions......Page 52
References......Page 53
Introduction......Page 54
Almost Random Projections with Margin Maximization......Page 56
Illustrative Examples......Page 59
Conclusions......Page 61
References......Page 62
Introduction......Page 63
Self-Organizing Maps for Structured Data......Page 64
The Activation Mask Kernel......Page 66
Adding Route Information to the Activation Mask Kernel......Page 67
Data Description and Experimental Setting......Page 69
Results and Discussion......Page 70
Conclusions......Page 71
References......Page 72
Introduction......Page 73
Tensor Product of Euclidean Spaces and Matrices......Page 74
Extension to Hilbert Spaces and Operators......Page 75
2-Way Operatorial Representation......Page 76
A General Class of Supervised Problems......Page 78
The Case of Scalar Outputs......Page 80
Experimental Results......Page 81
References......Page 82
Introduction......Page 84
Tri-Class Support Vector Machines......Page 85
Co-Training for Facial Expressions Annotation......Page 86
Setup......Page 87
Results and Discussion......Page 88
References......Page 89
Introduction......Page 90
Learning Prototypes (LPs)......Page 91
Learning SVs (LSVs)......Page 92
Experiments......Page 93
References......Page 95
Introduction......Page 96
Algorithms for Solving NPP and SVM......Page 97
Convergence of GSK and MDM......Page 98
Convergence of SMO......Page 100
References......Page 101
Introduction......Page 102
Feature Selection by Zero Norm Minimisation......Page 103
Support Feature Machine......Page 104
Experiments......Page 105
Implementation Issues......Page 106
References......Page 107
Introduction......Page 108
The Maze Structure......Page 109
Optimal Agent and Assumptions on Human Model......Page 110
Action Selection Probability......Page 111
Transition Probability......Page 112
Estimation for Belief State......Page 113
Result of Parameter Estimation......Page 114
Result of Belief State Estimation......Page 115
References......Page 116
Introduction......Page 118
Preliminaries: Temporal Learning and Reasoning......Page 119
Representing the Model......Page 120
Learning and Evolving the Model......Page 121
Extracting Knowledge about the Model......Page 122
Validation and Experiments: Case Study......Page 123
Integrating Knowledge Sources......Page 124
Case Study Discussion......Page 125
References......Page 127
Introduction......Page 128
Go and Capture Game......Page 129
Multi-Dimensional Recurrent Neural Networks......Page 130
Evolution Strategies......Page 131
Policy Gradients with Parameter-Based Exploration......Page 132
Network Topology......Page 133
Results......Page 134
Future Work......Page 135
References......Page 136
Introduction......Page 138
Motion Fields Described by Affine Models......Page 139
Extraction of Motion Layers with a Recurrent Neural Network......Page 140
Combined Segregation and Affine Model Estimation......Page 143
Results......Page 144
References......Page 146
Solution of the Weighted Regression......Page 147
Problem Formulation......Page 148
Illustrative Example – Tunnel Furnace......Page 149
References......Page 151
Introduction......Page 152
Formulation of Statistical Reconstruction Problem......Page 153
Experimental Results......Page 154
References......Page 155
Introduction......Page 156
Knowledge Structure Based on Statistical Language Analysis......Page 157
The Metaphor Evaluation Process......Page 158
Result of the Simulation......Page 159
References......Page 161
Introduction......Page 162
Attractor-Based Computation with Reservoir Networks......Page 163
Key Ingredients of Reservoir Computing......Page 164
On the Distribution of Attractor States......Page 166
References......Page 167
Introduction......Page 168
Feature Extraction for Action Representation......Page 169
Action Classification Using LSTM-RNN......Page 170
Experimental Results......Page 171
References......Page 173
Introduction......Page 174
The Model......Page 176
The Reward Signal......Page 177
Simulation and Results......Page 178
Discussion......Page 181
References......Page 182
Introduction......Page 184
Incremental Probabilistic Neural Network......Page 185
Learning Algorithm......Page 186
Reinforcement Learning......Page 188
Estimating the Outputs of a Complex Plant......Page 189
A Reinforcement Learning Task......Page 190
Predicting the Motor Actions in a Robotic Task......Page 191
References......Page 192
Introduction......Page 194
Model Overview......Page 196
Implementation Details......Page 197
Experiment I......Page 200
Experiment II......Page 201
References......Page 202
Introduction......Page 204
State of the Art......Page 205
Mathematical Foundations......Page 206
Our Algorithm......Page 208
Experiments......Page 211
References......Page 213
Reinforcement Learning and OCF......Page 214
Application......Page 215
References......Page 217
One-Shot Supervised Reinforcement Learning for Multi-targeted Tasks: RL-SAS......Page 218
Decomposition of Expected Rewards by Targets......Page 220
Simulation Results......Page 221
References......Page 223
Neuroanatomy of Birdsong......Page 224
Model Description......Page 225
The Motor Pathway Model......Page 226
The Vocal Filter Model......Page 227
Results......Page 228
References......Page 229
A Model to Demonstrate the Role of BG in Navigation......Page 230
An Integrated Model for Navigation......Page 233
References......Page 235
Introduction......Page 236
Application Domain......Page 237
Algorithm......Page 238
Experimental Evaluation of the Approach......Page 240
References......Page 241
Introduction......Page 242
Implementation of the Model......Page 243
The Simulation Results......Page 245
References......Page 246
Introduction......Page 248
Extracting the ‘Shape’ of a Visually Observed End Effector Movements (of Self and Others)......Page 250
Virtual Trajectory Synthesis and Learning to Shape......Page 252
Motor Command Synthesis: Coupled Interactions between the Virtual Trajectory and Internal Body Model......Page 255
References......Page 257
Introduction......Page 259
The Extended Kuramoto Model......Page 260
Methods......Page 261
Experiment 1......Page 263
Experiment 2......Page 266
Conclusion and Future Work......Page 267
References......Page 268
Introduction......Page 270
Brain Model......Page 272
The Computational Model......Page 273
Results......Page 276
Analysis......Page 277
References......Page 278
Introduction......Page 280
Properties of the Robot Model......Page 281
Artificial Neural Network Robot Model......Page 282
Application of Artificial Neural Network Model in Sliding Mode Control of Robot......Page 283
Experiments and Results......Page 285
References......Page 288
Introduction......Page 290
Quadruped Gaits......Page 291
Basic CPG Model......Page 292
Digital Hardware Implementation......Page 293
Module of Van Der Pol Oscillator......Page 294
Quadruped Gait Network Architecture......Page 295
Implementation Results......Page 296
Conclusions and Future Work......Page 298
References......Page 299
Introduction......Page 300
Evolutionary Strategies......Page 301
Trajectory Tracking Problem......Page 303
Kinematic Model of a WMR......Page 304
Trajectory-Tracking......Page 305
Sliding-Mode Trajectory-Tracking Control......Page 306
Experimental Results......Page 307
Conclusion......Page 308
References......Page 309
Introduction......Page 310
The Cognitive Model......Page 311
The Basic Memory and Encoding Perceptions......Page 312
Place Cells......Page 313
Building a Cognitive Topological Map of Environment......Page 315
Simulations and Results......Page 316
References......Page 319
Introduction......Page 321
The Hybrid Control Structure......Page 322
Leader-Following Formation Models......Page 324
Sliding-Mode Controller Design......Page 326
Simulation Results......Page 327
Conclusions......Page 329
References......Page 330
Introduction......Page 331
Motivated Sensorimotor Navigation......Page 332
Learning a Reinforcement Signal via Stimulation of a Non-specific Sensor......Page 334
Robotic Experiments: Learning Interactively to Reach a Goal When the Robot Is Lost......Page 337
Conclusions and Perspectives......Page 338
References......Page 339
Introduction......Page 341
The Algorithm for Maze Navigation and Topological MapCreation......Page 343
References......Page 346
Introduction......Page 347
First Stage: Minimum Information Learning......Page 348
Second Stage: Maximum Information Learning......Page 350
Third Stage: Maximum Information Relearning......Page 351
Results and Discussion......Page 352
References......Page 355
Introduction......Page 357
Modeling of Dynamics Using SOM on a Parameter Space......Page 358
Selection of a Parametric Model......Page 359
Visualization of Changes in Dynamic Behaviour......Page 360
Tank Level Control Dynamics......Page 361
Isolation of Chatter Effect in Vibration Data of a Rolling Mill......Page 363
References......Page 365
Introduction......Page 367
Structured Flows on Manifolds (SFMs)......Page 369
Functional Architectures......Page 370
Sequential Dynamics......Page 371
Implementation of Cursive Handwriting......Page 372
Discussion......Page 373
References......Page 374
Introduction......Page 376
Experiment Description......Page 378
Saccadic Onset Detection and ST-Data Collection......Page 379
Grouping STs with Neural-Gas acting on EOG-Velocity Patterns......Page 380
Between-Groups Comparison of Brain Dynamics......Page 381
Results......Page 383
References......Page 384
Introduction......Page 386
GNNs and PM–GraphSOMs......Page 387
The General Framework......Page 388
GNN and PM–GraphSOM Peculiarities......Page 389
A Layered Architecture for Web Spam Detection......Page 390
Experimental Results......Page 392
References......Page 394
Introduction......Page 396
Self-Organizing Maps (SOMs) and Contribution......Page 397
Comparison of the Labelling Versions......Page 400
Selection of the Number of Data Samples......Page 401
Selection of the Training Algorithm and its Parameters......Page 402
References......Page 404
Introduction......Page 406
Description of the Technique......Page 407
Definition of Maps of Dynamics for Time-Response Analysis......Page 409
Experiments and Results......Page 410
References......Page 414
Introduction......Page 416
Snap-Drift Algorithm......Page 417
SDSOM......Page 418
Results......Page 419
Conclusion......Page 422
References......Page 423
Introduction......Page 424
SOM Approach for Monitoring Fault Evolution......Page 425
Results and Discussion......Page 426
References......Page 427
Introduction......Page 428
System Description......Page 429
Linear Analysis......Page 430
Experiments......Page 431
Conclusion and Discussion......Page 432
References......Page 433
Introduction......Page 434
STRAGEN Algorithm......Page 435
Experiments......Page 436
Noise......Page 437
Three Gaits one Data Base......Page 438
References......Page 439
Introduction......Page 440
Self-Organising Map and Visualisations......Page 441
Minimum Spanning Tree Visualisation......Page 442
Experimental Evaluation......Page 443
References......Page 445
Introduction......Page 446
World Knowledge Representation......Page 447
Sentence Comprehension......Page 449
Experiments......Page 450
References......Page 451
Introduction......Page 452
ACD Approach......Page 453
Echo State Networks......Page 454
PHB Production Process......Page 455
Results and Discussion......Page 456
Conclusions......Page 459
References......Page 460
Introduction......Page 462
Background......Page 463
Description of the Data......Page 464
Representation of the Data......Page 465
Visualization Using Principal Component Analysis (PCA)......Page 466
Classifier Used......Page 467
Results for Channel 1......Page 468
Results for Channel 2......Page 469
References......Page 470
Introduction......Page 472
Detecting Changes Using the ICI rule......Page 473
Change-Detection Refinement Procedure......Page 474
ICI-Based Adaptive Classifier......Page 476
Experiments......Page 478
References......Page 480
Introduction......Page 482
Related Work......Page 483
Adaptive Metric Learning for Initialization Graph......Page 484
Graph Based Semi-Supervised Classification......Page 485
Experiments......Page 487
Experimental Results......Page 488
Discussion of lp Estimation......Page 490
Conclusion......Page 491
References......Page 492
Mathematical Model of the Longitudinal Dynamics......Page 493
Speed vs. Braking Distance......Page 494
Control System......Page 495
Genetic Optimization of the Controller......Page 496
Results......Page 498
References......Page 499
Introduction......Page 500
Local Fusion with Neural Networks......Page 501
Experimental Results......Page 503
Conclusions......Page 504
References......Page 505
Introduction......Page 506
System Architecture......Page 507
Selection of Relevant Visual and Auditory Segments......Page 508
Results......Page 509
References......Page 511
Introduction......Page 512
Problem Formulation and Model Description......Page 513
Global Convergence......Page 514
Simulation Results......Page 516
References......Page 518
Introduction......Page 520
HTM Formalism......Page 521
Proposed Extension for the HTM Formalism......Page 524
Example Application: Sign Language Recognition......Page 525
Discussion......Page 529
References......Page 531
Introduction......Page 533
Mutual Information......Page 534
Particle Swarm Optimization for ICA......Page 535
Results and Discussion......Page 536
References......Page 538
Introduction......Page 539
Surface Transformation......Page 540
Experimental Results and Discussion......Page 543
References......Page 544
Artificial Immune Networks......Page 545
Pareto Dominance......Page 546
The Pareto Cloud......Page 547
Evolution of the Network......Page 548
Experiments......Page 549
References......Page 550
Author Index......Page 551