Artificial Neural Networks in Pattern Recognition 4th IAPR TC3 Workshop ANNPR 2010 Cairo Egypt April 11 13 2010 Proceedings 1st Edition by Friedhelm Schwenker, ‎Neamat El Gayar ISBN 9783642121586 by Ahmed Al-ani, Amir F. Atiya (auth.), Friedhelm Schwenker, Neamat El Gayar (eds.) 9783642121586, 3642121586 instant download after payment.

Artificial Neural Networks in Pattern Recognition 4th IAPR TC3 Workshop ANNPR 2010 Cairo Egypt April 11 13 2010 Proceedings 1st Edition by Friedhelm Schwenker, ‎Neamat El Gayar - Ebook PDF Instant Download/Delivery: 9783642121586
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ISBN 13: 9783642121586
Author: Friedhelm Schwenker, ‎Neamat El Gayar

Artificial Neural Networks in Pattern Recognition synthesizes the proceedings of the 4th IAPR TC3 Workshop, ANNPR 2010. Topics include supervised and unsupervised learning, feature selection, pattern recognition in signal and image processing.

Artificial Neural Networks in Pattern Recognition 4th IAPR TC3 Workshop ANNPR 2010 Cairo Egypt April 11 13 2010 Proceedings 1st Table of contents:

1. Supervised Learning
2. Pattern Classification Using a Penalized Likelihood Method
3. Introduction
4. The Proposed Method
5. The Proposed Algorithm
6. Simulation Results
7. Conclusion
8. References
9. Evaluation of Feature Selection by Multiclass Kernel Discriminant Analysis
10. Introduction
11. Kernel Discriminant Analysis for Multiclass Problems
12. Selection Criteria
13. KDA Criterion for Multiclass Problems
14. New Between-Class Scatter
15. Feature Selection Methods
16. Backward Feature Selection
17. Block Deletion
18. Cross-Validation as a Stopping Condition
19. Performance Evaluation
20. Data Sets and Evaluation Conditions
21. Experimental Results
22. Conclusions
23. References
24. Correlation-Based and Causal Feature Selection Analysis for Ensemble Classifiers
25. Introduction
26. Related Research
27. Theoretical Approach
28. Feature Selection
29. Ensemble Classifier
30. Experimental Setup
31. Dataset
32. Evaluation
33. Experimental Result
34. Discussion
35. Conclusion
36. References
37. A New Monte Carlo-Based Error Rate Estimator
38. Introduction
39. Error Rate Estimators
40. Proposed Estimator
41. Experimental Setup
42. Experimental Results
43. Conclusions
44. References
45. Recognition of Sequences of Graphical Patterns
46. Introduction
47. Architecture and Training Algorithm
48. Demonstration
49. Conclusion and On-GoingWork
50. References
51. Maximum Echo-State-Likelihood Networks for Emotion Recognition
52. Introduction
53. The Maximum Echo-State-Likelihood Network
54. Dataset Description and Feature Extraction
55. Experiments
56. Conclusions
57. References
58. Robustness Analysis of Eleven Linear Classifiers in Extremely High–Dimensional Feature Spaces
59. Introduction
60. Classification
61. Linear Classifiers
62. Training Algorithms
63. Model Based Classifiers
64. Linear and Quadratic Programming Training Algorithms
65. Iterative Training Algorithms
66. Experimental Setup
67. Breakdown Experiments
68. Single Outlier Experiments
69. Experiments on Real Datasets
70. Results
71. Conclusion
72. References
73. Unsupervised Learning
74. Global Coordination Based on Matrix Neural Gas for Dynamic Texture Synthesis*
75. Introduction
76. Nonlinear Manifold Embedding Based on Matrix Neural Gas
77. Dynamic Texture Synthesis by System Traversal
78. Experiments
79. Discussion
80. References
81. SIC-Means: A Semi-fuzzy Approach for Clustering Data Streams Using C-Means
82. Introduction
83. Related Work
84. SIC-Means
85. Evaluation
86. Evaluation Scheme
87. Data
88. Experimental Results
89. Conclusion
90. Discussion and Future Work
91. References
92. The Mathematics of Divergence Based Online Learning in Vector Quantization
93. Introduction
94. Prototype Based Vector Quantization
95. Unsupervised Vector Quantization
96. Supervised Vector Quantization
97. Divergences as Dissimilarities and Derivatives Thereof
98. Basic Divergences
99. Parameter Adaptation in Divergence Based Learning
100. Relevance Learning for Positive Measures
101. Conclusion
102. References
103. Cluster Analysis of Cortical Pyramidal Neurons Using SOM
104. Introduction
105. Materials and Methods
106. Experimental Basis and Reconstructions
107. Editing and Conversion of Morphology Files
108. Terminology and Shape Characteristics
109. Feature Space Representation
110. Cluster Analysis
111. Results
112. Conclusion
113. References
114. Parallelized Kernel Patch Clustering
115. Introduction
116. Kernel Based Methods for Clustering
117. Kernel Patch Clustering
118. Parallelized Kernel Patch Clustering
119. Experiments and Results
120. Five Two-Dimensional Gaussian Distributed Cluster
121. Wisconsin Diagnostic Breast Cancer
122. Bag of Words – Enron Emails
123. Conclusion
124. References
125. Visual Pattern Recognition
126. Neural Network Cascade for Facial Feature Localization
127. Introduction
128. Overview
129. Face Detector
130. Coarse Localization
131. Precise Localization
132. Setup and Sensitivity Analysis
133. Coarse Localization
134. Precise Eye Localization
135. Precise Mouth Localization
136. Experimental Results
137. Results on the ISIR Database
138. Results on the Inrialpes, BioID and Feret Test Databases
139. Conclusion and Prospects
140. References
141. A Hidden Markov Model Based Approach for Facial Expression Recognition in Image Sequences
142. Introduction
143. Stochastic and Functional Principles
144. Hidden Markov Models
145. Gaussian Mixture Models
146. Classifier Fusion
147. Data Collection
148. Experiments and Results
149. Adjusting the Number of Gaussian-Components
150. Investigating Temporal Dependence
151. Results of the Single Models
152. Results of the Fused Models
153. Summary
154. References
155. Analysis, Interpretation, and Recognition of Facial Action Units and Expressions Using Neuro-Fuzzy M
156. Introduction
157. Facial Data Extraction and Representation
158. Biased Learning
159. Appearance-Based Facial Feature Extraction Using Gabor Wavelets
160. Geometric-Based Facial Feature Extraction Using Optical Flow
161. Facial Action Unit Recognition Using Neuro-Fuzzy Modeling
162. Takagi-Sugeno Fuzzy Inference System and Training Data Set
163. Structure Identification Algorithm
164. Parameter Identification Using Hybrid Learning
165. Experimental Results
166. Discussion and Conclusions
167. References
168. Content-Based Retrieval and Classification of Ultrasound Medical Images of Ovarian Cysts
169. Introduction
170. Feature Extraction
171. Histogram Moments
172. GLCM Based Texture Feature
173. Feature Fusion and Normalization
174. Image Retrieval and Classification
175. Similarity Model for Image Retrieval
176. Image Classification Using Fuzzy k-NN
177. Performance Analysis
178. Retrieval Performance
179. Classification Performance
180. Conclusions
181. References
182. Applications
183. A Novel Word Spotting Algorithm Using Bidirectional Long Short-Term Memory Neural Networks
184. Introduction
185. Neural Network Based Handwritten Text Recognizer
186. Preprocessing
187. BLSTM Neural Network
188. Word Spotting Using BLSTM
189. CTC Token Passing Algorithm
190. Experimental Evaluation
191. Setup
192. Results
193. Conclusion
194. References
195. Swarm Based Fuzzy Discriminant Analysis for Multifunction Prosthesis Control
196. Introduction
197. Methodology
198. Weighted Fuzzy Discriminant Analysis
199. PSO Based Weight Optimization
200. Experiments and Results
201. Conclusion
202. References
203. Bayesian Learning of Generalized Gaussian Mixture Models on Biomedical Images
204. Introduction
205. Bayesian Estimation of the GGM Model
206. Experimental Results
207. Biomedical Image Segmentation
208. Spot Detection and Image Segmentation in DNA Microarray Data
209. Conclusion
210. References
211. Defective Areas Identification in Aircraft Components by Bivariate EMD Analysis of Ultrasound Signal
212. Introduction
213. The Proposed Approach
214. Experimental Setup
215. Experimental Results
216. References
217. Different Regions Identification in Composite Strain-Encoded (C-SENC) Images Using Machine Learning
218. Introduction
219. Theory of C-SENC
220. Proposed System
221. Experimental Results
222. Simulated Data Results
223. Real MR Data Results
224. Summary and Conclusion
225. References
226. Exploiting Neural Networks to Enhance Trend Forecasting for Hotels Reservations
227. Introduction
228. Forecasting Arrivals and Occupancy Using Simulation
229. NNinForecasting
230. Proposed Trend Component Forecasting Using NN
231. Prepare Inputs
232. Adjust Parameters and Train Neural Networks
233. Forecast Trend Using Trained NNs
234. Experiments and Results
235. Conclusions
236. References
237. VLSI Architecture of the Fuzzy Fingerprint Vault System
238. Introduction
239. Fuzzy Fingerprint Vault
240. Hardware Architecture of the Fuzzy Fingerprint Vault
241. Experimental Results
242. Conclusion
243. References
244. Invited Talk
245. Clustering Very Large Dissimilarity Data Sets
246. Introduction
247. Relational Self-Organizing Maps
248. Standard Batch SOM and Corresponding Cost Function
249. Pseudo-Euclidean Embedding of Dissimilarity Data
250. Relational SOM
251. Convergence
252. Dual Cost Function
253. Patch Processing
254. Experiments
255. Mapping MIDI Files
256. Mapping Newsgroups
257. Conclusions
258. References
259. Author Index

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Tags: Friedhelm Schwenker, Neamat El Gayar, Artificial, Networks