Conference Information
ICMLA 2021: International Conference on Machine Learning and Applications
Submission Date:
2021-07-12 Extended
Notification Date:
Conference Date:
Pasadena, California, USA
CORE: c   QUALIS: b2   Viewed: 27713   Tracked: 42   Attend: 12

Call For Papers
Scope of the Conference:

•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
•evolutionary computation
•machine learning and natural language processing
•multi-lingual knowledge acquisition and representation
•grammatical inference
•knowledge discovery in databases
•knowledge Intensive Learning
•machine learning and information retrieval
•machine learning for bioinformatics and computational biology
•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 modelling
•adversarial learning
•machine leaning privacy
•hybrid learning algorithms
•deep learning
•big data
•machine learning in:
o game playing and problem solving
o intelligent virtual environments
o industrial and engineering applications
o homeland security applications
o medicine, bioinformatics and systems biology
o economics, business and forecasting applications

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.
Last updated by Dou Sun in 2021-07-03
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