Scope of the Conference: 


   - multistrategy learning
   - statistical learning
   - neural network learning
   - learning through fuzzy logic
   - learning through evolution (evolutionary algorithms)
   - bayesian network
   - case-based reasoning
   - evolutionary computation
   - reinforcement learning
   - machine learning of natural language
   - grammatical inference
   - knowledge acquisition and learning
   - knowledge discovery in databases
   - knowledge intensive learning
   - knowledge representation and reasoning
   - information retrieval and learning
   - theories and models for plausible reasoning
   - cooperative learning
   - planning and learning
   - multi-agent learning
   - web navigation and mining
   - online learning
   - learning through text mining
   - inductive learning
   - inductive logic programming
   - feature extraction and classification
   - support vector machines
   - computational learning theory
   - cognitive-modeling
   - hybrid algorithms
   - machine learning in game playing and problem solving
   - machine learning in intelligent virtual environments
   - machine learning in medicine
   - machine learning in biology
   - machine learning in industrial applications
Contributions describing applications of machine learning techniques to real-world problems, interdisciplinary research involving machine learning, experimental and/or theoretical studies yielding new insights into the design of ML systems, and papers describing development of new analytical frameworks that advance practical learning methods are especially encouraged.