Connectionists: MLMTA'07 - LAST CALL FOR PAPERS

[Matthias Dehmer]

#####################################################################
                  CALL FOR PAPERS
#####################################################################
 MLMTA'07 - The 2007 International Conference on Machine Learning: Models,
Technologies & Applications
 Monte Carlo Resort, Las Vegas, Nevada, USA
                  June 25-28, 2007
 http://www.world-academy-of-science.org/worldcomp07
#####################################################################

The 2007 International Conference on Machine Learning: Models,
Technologies & Applications (MLMTA'07) will be held in Las Vegas,
Nevada, June 25-28, 2007.


MLMTA'07 aims to bring together researches from computer science, applied
statistics, applied mathematics and engineering working in the field of
Machine Learning. In addition to traditional topics in development and
application of statistical methods in data analysis MLMTA'07 has this year
a special focus on methods utilizing the 'systems view' of a problem. Due
to the fact that graph-based methods have proven to be an useful
mathematical representation of problems in this class submitted papers
developing or applying graph-based statistical methods are of utmost
interest.


TOPICS OF INTEREST include, but are not limited to:

          o General Machine Learning Theory
                      # Statistical learning theory
                      # Unsupervised and Supervised Learning
                      # Multivariate analysis
                      # Hierarchical learning models
                      # Relational learning models
                      # Bayesian methods
                      # Meta learning
                      # Stochastic optimization
                      # Simulated annealing
                      # Heuristic optimization techniques
                      # Neural networks
                      # Evolutionary algorithms in learning
                      # Reinforcement learning
                      # Multi-criteria reinforcement learning
                      # General Learning models
                      # Multiple hypothesis testing
                      # Decision making
                      # Markov chain Monte Carlo (MCMC) methods
                      # Non-parametric methods
                      # Graphical models
                      # Gaussian graphical models
                      # Bayesian networks
                      # Sequential Monte Carlo methods
                      # Particle filter
                      # Time series prediction
                      # Fuzzy logic and learning
                      # Inductive learning and applications
                      # Grammatical inference
          o General Graph-based Machine Learning Techniques
                      # Graph kernel and graph distance methods
                      # Graph-based semi-supervised learning
                      # Graph clustering
                      # Graph learning based on graph transformations
                      # Graph learning based on graph grammars
                      # Graph learning based on graph matchings
                      # General theoretical aspects of graph learning
                      # Statistical modeling of graphs
                      # Information-theoretical approaches of graphs
                      # Motif search
                      # Network inference
                      # General issues in graph and tree mining
          o Machine Learning Applications
                      # Aspects of knowledge structures
                      # Computational Finance
                      # Computational Intelligence
                      # Knowledge acquisition and discovery techniques
                      # Induction of document grammars
                      # Supervised and unsupervised classification of web
                        data
                      # General Structure-based approaches in information
                        retrieval
                      # General Structure-based approaches in web authoring
                      # General Structure-based approaches in information
                        extraction
                      # General Structure-based approaches in web content
                        mining
                      # Graph and tree mining approaches for analyzing
                        web-based document structures
                      # Analysis of link structures
                      # Latent semantic analysis
                      # Aspects of natural language processing
                      # Categorization of web-based units
                      # Aspects of text technology
                      # Computational linguistics and application
                      # Computational vision
                      # Bioinformatics
                      # Biostatistics
                      # Computational Biology
                      # High-throughput data analysis
                      # Biological network analysis:
                            * protein-protein networks
                            * signaling networks
                            * metabolic networks
                            * transcriptional regulatory networks
                      # Graph Inference based on biological data
                      # Graph-based models in biostatistics
                      # Optimization methods in bioinfomatics and
                        biochemistry
                      # Speech and Signal Processing
                      # Computational Neuroscience
                      # Computational Chemistry
                      # Computational Statistics
                      # Systems Biology
                      # Algebraic Biology
                      # Further applications of ML-methods in chemistry,
                        biomedical analysis
                      #  computer vision, and neuroscience



IMPORTANT DATES

* March 20, 2007 - Draft papers due
* April 20, 2007 - Notification of acceptance
* May 21, 2007 - Camera ready papers & pre-registration due
* June 25-28, 2007 - MLMTA'07


Please visit the conference website at
http://www.bio-complexity.com/MLMTA_index.html


Conference Organization

* Hamid R. Arabnia, University of Georgia, Georgia, USA
* Frank Emmert-Streib, University of Washington, Seattle, USA
* Matthias Dehmer, Max F. Perutz Laboratories, Vienna Bio Center, Vienna,
Austria

For more information email: MLMTA || bio-complexity.com
or visit the conference website
http://www.bio-complexity.com/MLMTA_index.html

############################################################################