Machine learning research in the Computing Laboratory is mainly concerned with:
- The theory and application of Computational Intelligence Techniques: Artificial Neural Networks (ANNs), Fuzzy and Neuro-Fuzzy Systems, Genetic Algorithms and Hybrid Intelligent Systems. The principal research contact for these areas is Vasile Palade.
- Applications of Symbolic Machine Learning to real-world problems including the performance of computer systems (including the Grid), applied computer security, and other areas. Techniques used range from Inductive Logic Programming (ILP) to decision-tree techniques. The principal research contact for these areas is Steve Moyle.
- Past activities, have been focused on the theory, implementation, and application of Inductive Logic Programming (ILP). Application areas included those in drug design, bioinformatics and chemoinformatics. Non-ILP research was concerned with the use of symbolic machine learning like decision-tree techniques to interesting real-world problems. The principal research for these areas was performed by past members including Ashwin Srinivasan. Aswhin's ILP system Aleph can be found here.
