
The lectures address the following key topics in algorithmic learning: statistical learning theory, kernel methods, boosting, reinforcement learning, theory learning, association rule learning, and learning linear classifier systems. Thus, the book is well balanced between classical topics and new approaches in machine learning.
Advanced students and lecturers will find this book a coherent in-depth overview of this exciting area, while researchers will use this book as a valuable source of reference.
| c a bartzokas a heck j m synge n a dyson michael a hogg | b a bengtsson william henry drummond honorac de balzac z a rizk h gronfold |