Artificial Neural Networks in Pattern Recognition ANNPR 2014

October 6-8, 2014, Concordia University, Montreal, Canada

Conference program

Download the complete conference proceedings

All sessions are oral presentations and will take place on the second floor (EV 2.260 and EV 2.184) in Concordia University EV building located at 1515 St. Catherine St. West above Guy metro station (green line).

Day 1

DAY 1 - Monday October 6, 2014
8.00 - 9.00	Registration
9.00 - 9.15	Opening & Welcome Speech
9.15 -10.15	IAPR Invited Speaker: Zhi-Hua Zhou Session Chair: Dr. Ching Suen Title: Large Margin Distribution Learning Abstract: Support vector machines (SVMs) and Boosting are possibly the two most popular learning approaches during the past two decades. It is well known that the margin is a fundamental issue of SVMs, whereas recently the margin theory for Boosting has been defended, establishing a connection between these two mainstream approaches. The recent theoretical results disclosed that the margin distribution rather than a single margin is really crucial for the generalization performance, and suggested to optimize the margin distribution by maximizing the margin mean and minimizing the margin variance simultaneously. Inspired by this recognition, we advocate the large margin distribution learning, a promising research direction that has exhibited superiority in algorithm designs to traditional large margin learning.
10.15 - 10.45	Coffee Break
10.45 - 12.25	Session 1 : Learning and Architectures Session Chair: Robert Sabourin A Decorrelation Approach for Pruning of Multilayer Perceptron Networks Hazem M. Abbas Combining Bipartite Graph Matching and Beam Search for Graph Edit Distance Approximation Kaspar Riesen, Andreas Fischer, Horst Bunke Hidden Markov Models Based on Generalized Dirichlet Mixtures for Proportional Data Modeling Elise Epaillard, Nizar Bouguila Linear Contrast Classifiers in High-Dimensional Spaces Florian Schmid, Ludwig Lausser, Hans A. Kestler
12.30 - 14.00	Lunch
14.00 - 15.15	Session 2 : Ensembles Session Chair: Hazem Abbas Dynamic Weighted Fusion of Adaptive Classifier Ensembles Based on Changing Data Streams Christophe Pagano, Eric Granger, Robert Sabourin, Gian Luca Marcialis, Fabio Roli Forward and Backward Forecasting Ensembles for the Estimation of Time Series Missing Data Tawfik A. Mohamed, Neamat El Gayar, Amir F. Atiya Analyzing Dynamic Ensemble Selection Techniques Using Dissimilarity Analysis Rafael M. O. Cruz, Robert Sabourin, George D. C. Cavalcanti
15.15 - 15.45	Coffee Break
15.45 - 17.00	Session 3: Ensembles & Applications Session Chair: George Cavalcanti Intelligent Ensemble Systems for Modeling NASDAQ Microstructure: A Comparative Study Salim Lahmiri Face Recognition Based on Discriminative Dictionary with Multilevel Feature Fusion Hongjun Li, Nicola Nobile, Ching Y. Suen A Time Series Classification Approach for Motion Analysis Using Ensembles in Ubiquitous Healthcare Systems Rana Salaheldin, Mohamed ElHelw, Neamat El Gayar
18.00 - 20.00	Welcome reception at Concordia University, room EV 11.725

Day 2

DAY 2 - Tuesday, October 7, 2014
9.00 - 10.00	IAPR Invited Speaker: Yoshua Bengio Session Chair: Friedhelm Schwenker Title: Deep Learning Abstract: Deep learning has arisen around 2006 as a renewal of neural networks research allowing such models to have more layers. Theoretical investigations have shown that functions obtained as deep compositions of simpler functions can express highly varying functions (with many ups and downs and different input regions that can be distinguished) much more efficiently (with fewer parameters) than otherwise. Empirical work in a variety of applications has demonstrated that, when well trained, such deep architectures can be highly successful, remarkably breaking through previous state-of-the-art in many areas, including speech recognition, object recognition, language models, and transfer learning. This talk will summarize the advances that have made these breakthroughs possible, and end with questions about some major challenges still ahead of researchers in order to continue our climb towards AI-level competence.
10.00 - 10.30	Coffee Break
10.30 - 12.10	Session 1 : Support Vector Machines & Applications Session Chair: Adam Krzyzak A New Multi-Class Fuzzy Support Vector Machine Algorithm Friedhelm Schwenker, Markus Frey, Michael Glodek, Markus Kächele, Sascha Meudt, Martin Schels and Miriam Schmidt Incremental Feature Selection by Block Addition and Block Deletion Using Least Squares SVRs Shigeo Abe Majority-Class Aware Support Vector Domain Oversampling for Imbalanced Classification Problems Markus Kächele, Patrick Thiam, Günther Palm and Friedhelm Schwenker Prediction of Insertion-Site Preferences of Transposons Using Support Vector Machines and Artificial Neural Networks Maryam Ayat, Michael Domaratzki
12.10 - 14.00	Lunch
14.00 - 15.15	Session 2: Character Recognition Session Chair: Andreas Fischer End-Shape Recognition for Arabic Handwritten Text Segmentation Amani T. Jamal, Nicola Nobile, Ching Y. Suen Part-Based High Accuracy Recognition of Serial Numbers in Bank Notes Bo-Yuan Feng, Mingwu Ren, Xu-Yao Zhang, Ching Y. Suen Investigating of Preprocessing Techniques and Novel Features in Recognition of Handwritten Arabic Characters Ahmed T. Sahlol, Ching Y. Suen, Abdelhay A. Sallam, Mohammed R. Elbasyoni
15.15 - 15.45	Coffee Break
15.45 - 17.00	Session 3: Image Processing Session Chair: Tony Kasvand Automatic Bridge Crack Detection { A Texture Analysis-Based Approach } Sukalpa Chanda, Guoping Bu, Hong Guan, Jun Jo, Umapada Pal, Yew-Chaye Loo, Michael Blumenstein Comparative Study of Feature Selection for White Blood Cell Differential Counts in Low Resolution Images Mehdi Habibzadeh, Adam Krzyzak, Thomas Fevens Low-dimensional Data Representation in Data Analysis Alexander Bernstein, Alexander Kuleshov
18.00 - 21.00	Dinner at the Vieux-Port Steakhouse in old Montreal (39 Rue Saint Paul Est, Montréal, QC H2Y 1G2, Canada)

Day 3

DAY 3 - Wednesday, October 8, 2014
9.00 - 10.00	IAPR Invited Speaker: J. Michael Herrmann Session Chair: Neamat El Gayar Title: Pattern Formation and Pattern Recognition: Active Learning in Biologically Inspired Robotics Abstract: The concept of self-organised criticality (SOC) describes the dynamics of systems as diverse as sandpiles, earthquakes, evolutionary systems, animal swarms, financial markets and the brain. It is particularly interesting in neural networks where SOC has been shown to enable optimal transmission and storage of information, and to improve sensing and control which is particularly useful for applications in autonomous robots. Operation at or near a critical point enables a robot to generate a sequence of optimal training data for learning a model of the environment. This active learning procedure can be used also as an organisational principle for a deep neural control architecture in a robot which serves the double function of generating input patterns by exploratory behaviour and recognising environmental perturbations of the self-generated patterns. The dynamics of such an adaptive control system is analysed in theory, in robotic experiments and discussed as an abstract model of brain-style control of behaviour. Following the idea of guided self-organisation, we show how pattern formation in critical systems can improve learning capabilities if, as often in applications, an external evaluative signal is available.
10.00 - 10.30	Coffee Break
10.30 - 12.10	Session 1: Learning II Session Chair: Shigeo Abe Unsupervised Active Learning of CRF Model for Cross-Lingual Named Entity Recognition Mohamed Farouk Abdel Hady, Abubakrelsedik Karali, Eslam Kamal, Rania Ibrahim A Reinforcement Learning Algorithm to Train a Tetris Playing Agent Patrick Thiam, Viktor Kessler, Friedhelm Schwenker Bio-Inspired Optic ow From Event-Based Neuromorphic Sensor Input Stephan Tschechne, Roman Sailer, Heiko Neumann Computing Upper and Lower Bounds of Graph Edit Distance in Cubic Time Kaspar Riesen, Andreas Fischer, Horst Bunke
12.10 -14.00	Closing Remarks

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