Pattern recognition is one the vital field in information science. Machine learning is developed in technology. However, those subjects are viewed as two facts of constant field. each in along has well-versed a speedy development over the past years. theorem ways have big from skilled keyword to become thought, whereas graphical models have emerged as a general framework for describing and applying probabilistic models. The attainable relevance of those ways has been improved through the event of a scale of approximate reasoning algorithms like variational Bayes and expectation propagation. New models supported components have had a notable impact on each algorithm and applications.
- Track 1-1 Pattern analysis
- Track 2-2 Data network connection
- Track 3-3 Linear and nonlinear system
- Track 4-4 Pattern control