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ICMLA 2014 : International Conference on Machine Learning and Applications


Conference Series : International Conference on Machine Learning and Applications
When Dec 3, 2014 - Dec 6, 2014
Where Detroit
Submission Deadline Jul 6, 2014
Notification Due Sep 7, 2014
Final Version Due Oct 1, 2014

Call For Papers

Call for papers

The 13th International Conference on Machine Learning and Applications (ICMLA'14) will be held in Detroit, MI, USA, December 3 – December 6, 2014.

The aim of the conference is to bring together researchers working in the areas of machine learning and applications. The conference will cover both theoretical and experimental research results. Submission of machine learning papers describing machine learning applications in fields like medicine, biology, industry, manufacturing, security, education, virtual environments, game playing and problem solving is strongly encouraged.

Contributions describing applications of machine learning (ML) 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 machine learning methods are especially encouraged.
Topics of interest include, but are not limited to, the following (alphabetically ordered):

case-based reasoning
cognitive modeling
computational learning theory
cooperative learning
deep learning
distributed and parallel learning algorithms and applications
evolutionary computation
feature extraction and classification
grammatical inference
hybrid learning algorithms
inductive learning
inductive logic programming
knowledge acquisition and learning
knowledge discovery in databases
knowledge intensive learning
knowledge representation and reasoning
learning through evolution (evolutionary algorithms)
learning through fuzzy logic
learning through mobile data mining
machine learning and information retrieval
machine learning and natural language processing
machine learning for bioinformatics and computational biology
machine learning for web navigation and mining
multi-agent learning
multi-lingual knowledge acquisition and representation
multistrategy learning
neural network learning
online and incremental learning
planning and learning
probabilistic models (e.g. Bayesian networks)
reinforcement learning
scalability of learning algorithms
statistical learning
support vector machines
text and multimedia mining through machine learning
theories and models for plausible reasoning

Applications of machine learning in:

bioinformatics, medicine and systems biology
economics, business and forecasting applications
energy applications
game playing and problem solving
homeland security applications
intelligent virtual environments
industrial and engineering applications

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