Chapter 1 APPROACHES IN MACHINE LEARNING

Machine learning deals with programs that learn from experience, i.e. programs that improve or adapt their performance on a certain task or group of tasks over time. In this tutorial, we outline some issues in machine learning that pertain to ambient and computational intelligence. As an example, we consider programs that are faced with the learning of tasks or concepts which are impossible to learn exactly in finitely bounded time. This leads to the study of programs that form hypotheses that are ‘probably approximately correct ’ (PAC-learning), with high probability. We also survey a number of meta-learning techniques such as bagging and adaptive boosting, which can improve the performance of machine learning algorithms substantially