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ICMLA 2019 aims to bring together researchers and practitioners to present their latest achievements and innovations in the area of machine learning (ML).
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.
The technical program will consist of, but is not limited to, the following 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
Applications of machine learning in:
medicine, health, bioinformatics and systems biology
industrial and engineering applications
security applications
smart cities
game playing and problem solving
intelligent virtual environments
economics, business and forecasting applications, etc.
The conference will include a number of interesting keynote plenary talks, which will be announced on the conference web site as arrangements are finalized. Previous invited speakers included numerous fellows of IEEE, AMIA, AAAS, AAAI, etc.
Paper Submission
High quality papers in all Machine Learning areas are solicited. Papers that present new directions in ML will receive careful reviews. Authors are expected to ensure that their final manuscripts are original and are not appearing in other publications. Paper should be limited to 8 pages and submitted in IEEE format (double column). Papers will be reviewed by the Program Committee on the basis of technical quality, originality, significance and clarity. All submissions will be handled electronically. Accepted papers will be published in the conference proceedings, as a hardcopy. Authors of the accepted papers need to present their papers at the conference. A selected number of accepted papers will be invited for possible inclusion, in an expanded and revised form, in some journal special issues.
ICMLA'19 Best Paper Award and ICMLA'19 Best Poster Award will be conferred at the conference to the authors of the best research paper and best poster presentation, respectively, based on the reviewers and Programme Committee recommendations.
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