The MLPMEA 2016 special session provides an excellent international forum for sharing knowledge and results in theory, methodology and applications of Machine Learning for developing predictive models for different engineering applications. Machine Learning models are efficient for handing complex prediction models due to their outstanding performance in handling large scale datasets with uniform characteristics and noisy data. Examples of MLPMEA 2016 topics of interest include building predictive models using Machine Learning to solve specific engineering problems such as regression and classification problems. The aim of this work is to obtain a good perspective into the current state of practice of Machine Learning to address various predictive problems.
Some topics relevant to this session include, but are not limited to:
Biomedical image analysis/processing
Clustering
Decision Support
Support Vector Machine
Time Series
Decision Trees
Fuzzy Logic & Systems
Probabilistic Reasoning
Lazy Learning
Classification
Recommender Systems
Expert Systems
Artificial Neural Networks
Evolutionary AlgorithmsRanking Algorithms
Cognitive Processes
Evolutionary Computing
Swarm Intelligence
Artificial Immune Systems
Markov Model
Chaos Theory
Multi-Valued Logic
Ensemble Techniques
Hybrid Intelligent Models
Reasoning Models
Applied to:
Nuclear Engineering
Sustainable and Renewable Energy
Software Engineering
Biomedical Engineering
Mechanical Engineering
Civil Engineering
Electrical Engineering
Computer Engineering
Chemical Engineering
Industrial Engineering
Environmental Engineering
12月18日
2016
12月20日
2016
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