The 13th International Conference on Machine Learning and Applications (ICMLA'14) will be held in Detroit, MI, USA, December 3 – December 5, 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
• cost-sensitive 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 from semi-structured data
• 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
• model calibration and reuse
• model evaluation
• 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
• transfer learning

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
• multimedia data (image, video, sound)
• social networks and web data