Machine Learning and Inference Laboratory
The Machine Learning and Inference (MLI) Laboratory conducts fundamental and experimental research on the development of intelligent systems capable of advanced forms of learning, inference, and knowledge generation, and applies them to real-world problems.
Major research areas include:
- theory and computational models of learning and inference
- data mining and knowledge discovery
- machine learning and natural induction
- inductive databases and knowledge scouts
- behavior modeling and computer intrusion detection
- non-Darwinian evolutionary computation
- multistrategy learning and knowledge mining
- intelligent systems for education
- models of human plausible reasoning
- machine vision with learning capabilities
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Recent Submissions
- Progress Report on the Learnable Evolution Model
- Semantic and Syntactic Attribute Types in AQ Learning
- A Measure of Description Quality for Data Mining and its Implementation in the AQ18 Learning System
- AQ-PM: A Method for Partial Memory Learning
- Discovering Multidimensional Patterns in Large Datasets Using Knowledge Scouts