18^th IEEE International Conference on Machine Learning and Applications (ICMLA 2019)

Dec 16-19, 2019 , Boca Raton, Florida, USA

Scope & Topics

The conference provides a leading international forum for the dissemination of original research in machine learning, with emphasis on applications as well as novel algorithms and systems.

Following the success of previous ICMLA conferences, the conference aims to attract researchers and application developers from a wide range of ML related areas, and the recent emergence of Big Data processing brings an urgent need for machine learning to address these new challenges. The conference will cover both machine learning theoretical research and its applications.

Contributions describing machine learning techniques applied to real-world problems and interdisciplinary research involving machine learning, in fields like medicine, biology, industry, manufacturing, security, education, virtual environments, games, are especially 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 :

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

Important Dates

Submission Deadline	:	September 07, 2019
Authors Notification	:	October 07, 2019
Final Manuscript Due	:	October 17, 2019

 

User Name : austin
Posted 12-12-2018 on 21:48:05 AEDT

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