SCOPE OF THE CONFERENCE
The aim of the conference is to bring researchers working in the
areas of machine learning and applications together. 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.
TOPICS OF INTEREST
Statistical Learning
Neural Network Learning
Learning Through Fuzzy Logic
Learning Through Evolution (evolutionary algorithms)
Reinforcement Learning
Multistrategy 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 Modeling
Hybrid Learning Algorithms
Deep Learning
Big data
Machine learning in:
Game playing and problem solving
Intelligent Virtual Environments
Industrial and Engineering Applications
Homeland Security Applications
Medicine, Bioinformatics and Systems Biology
Economics, Business and Forecasting Applications
APPLICATION OF MACHINE LEARNING
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.
