You are invited to submit a full paper for consideration. All accepted papers will be published in the conference proceedings/book.

Topics of interest include, but are not limited to, the following:

Brain models / cognitive science
Natural language processing
Fuzzy logic and soft computing
Software tools for AI
Expert systems
Decision support systems
Automated problem solving
Knowledge discovery
Knowledge representation
Knowledge acquisition
Knowledge-intensive problem solving techniques
Knowledge networks and management
Intelligent information systems
Intelligent data mining and farming
Intelligent web-based business
Intelligent agents
Intelligent networks
Intelligent databases
Intelligent user interface
AI and evolutionary algorithms
Intelligent tutoring systems
Reasoning strategies
Distributed AI algorithms and techniques
Distributed AI systems and architectures
Neural networks and applications
Heuristic searching methods
Languages and programming techniques for AI
Constraint-based reasoning and constraint programming
Intelligent information fusion
Learning and adaptive sensor fusion
Search and meta-heuristics
Multisensor data fusion using neural and fuzzy techniques
Integration of AI with other technologies
Evaluation of AI tools
Social intelligence (markets and computational societies)
Social impact of AI
Emerging technologies
Applications (including: computer vision, signal processing, military, surveillance, robotics, medicine, pattern recognition, face recognition, finger print recognition, finance and marketing, stock market, education, emerging applications, ...)
The 2009 International Workshop on Machine Learning; Models, Technologies and Applications:
- 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
. 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
. Particle filter
. Cross-Entropy method
. Ant colony optimization
. Time series prediction
. Fuzzy logic and learning
. Inductive learning and applications
. Grammatical inference

- 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 matching
. General theoretical aspects of graph learning
. Statistical modeling of graphs
. Information-theoretical approaches to graphs
. Motif search
. Network inference
. General issues in graph and tree mining

- 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, web authoring, information extraction, and web content mining
. Latent semantic analysis
. Aspects of natural language processing
. Intelligent linguistic
. Aspects of text technology
. Computational vision
. Bioinformatics and computational biology
. Biostatistics
. High-throughput data analysis
. Biological network analysis: protein-protein networks, signaling networks, metabolic networks, transcriptional regulatory networks
. Graph-based models in biostatistics
. Computational Neuroscience
. Computational Chemistry
. Computational Statistics
. Systems Biology
. Algebraic Biology