MLMTA'06 - The 2006 International Conference on Machine Learning; Models, Technologies & Applications
Last modified
2007-12-02 08:43
Monte Carlo Resort, Las Vegas, Nevada, USA
(June 26-29, 2006)
MLMTA'06 is an international conference held simultaneously
(ie, same location and dates) with a number of other
joint conferences as part of WORLDCOMP'06 (The 2006 World
Congress in Computer Science, Computer Engineering, and
Applied Computing). WORLDCOMP'06 is the largest annual
gathering of researchers in computer science, computer
engineering and applied computing. Many of the joint conferences in WORLDCOMP are the premier
conferences for presentation of advances in their respective
fields (for the complete list of joint conferences Click Here).
The motivation is to assemble a spectrum of affiliated
research conferences into a coordinated research meeting
held in a common place at a common time. The main goal
is to provide a forum for exchange of ideas in a number
of research areas that interact. The model used to form
these annual conferences facilitates communication among
researchers in different fields of computer science,
computer engineering and applied computing. Both inward
research (core areas of computer science and engineering)
and outward research (multi-disciplinary, Inter-disciplinary,
and applications) will be covered during the conferences.
The last set of conferences (MLMTA'05 and affiliated events) had
research contributions from 76 countries and had attracted over 1,500 participants. It is anticipated to have over 2,000 participants for
the 2006 event.
You are invited to submit a draft paper of about 5-8 pages and/or a proposal to
organize a Technical Session/workshop (see the Submission information).
All accepted papers will be published in the respective
conference proceedings. The names of technical session/workshop
organizers/chairs will appear on the cover of the
proceedings/books as Associate Editors.
Topics of interest include, but are not limited to,
the following:
- Machine learning in problem solving
- Learning models
- Artificial neural networks and learning
- Fuzzy logic and learning
- Inductive learning and applications
- Learning by examples
- Statistical methods in learning
- Evolutionary algorithms in learning
- Reinforcement learning methods
- Multi-agent learning
- Hierarchical learning models
- Collaborative learning and filtering
- ODE Methods and machine learning
- Multi-criteria reinforcement learning
- Relational learning models
- Speedup learning techniques
- Computational needs of learning models
- Formal learning methods
- Graph-based learning
- Learning based on adaptive techniques
- Learning topological maps
- Learning in planning
- Query learning
- Active learning
- Memory-based learning
- Instance-based learning
- Life-long learning
- Q-Learning
- Predictive learning models
- Information retrieval and data mining
- Knowledge representation and management
- Knowledge acquisition and discovery techniques
- Bayesian-based methodologies
- Grammatical inference
- Cognitive modeling
- Case-based reasoning
- Semantic indexing
- Natural language processing
- Machine translation
- Temporal abstractions
- Feature selection and classification
- Theory refinement methodologies
- Probabilistic reasoning
- Self-adaptation techniques
- Game playing (chess, ...)
- Text categorization and classification
- Machine learning applications (medicine, games, biology,
industrial applications, robotics, security and terrorism, ...)
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