Support vector machines (SVMs) are a useful tool for learning classifiers or function approximations from data [8, 15, 16, 2, 17, 3, 9]. One important interpretation of SVMs is as an optimization problem in a reproducing-kernel Hilbert space (RKHS) [20, 21]. Recently, prior knowledge in the form of inequalities which must be satisfied over sets of the input space have been added to SVMs for bot...

There are many diierent ways of representing knowledge, and for each of these ways there are many diierent discovery algorithms. How can we compare diierent representations? How can we mix, match and merge representations and algorithms on new problems with their own unique requirements? This chapter introduces probabilistic modeling as a philosophy for addressing these questions and presents g...

Knowledge elicitation cannot be lead solely by the expert. Elicited knowledge must be analysed and represented by the knowledge engineer. When a knowledge engineering project is started some framework must exist of what is likely to be encountered and the representation to be used for it. Given our present knowledge of the psychology of expertise the top-down influence on knowledge elicitation ...

In this talk we survey work being conducted at the Centre for Integrative Systems Biology at Imperial College on the use of machine learning to build models of biochemical pathways. Within the area of Systems Biology these models provide graph-based descriptions of bio-molecular interactions which describe cellular activities such as gene regulation, metabolism and transcription. One of the key...

Bayesian Knowledge Tracing (BKT)[1] is a user modeling method extensively used in the area of Intelligent Tutoring Systems. In the standard BKT implementation, there are only skill-specific parameters. However, a large body of research strongly suggests that studentspecific variability in the data, when accounted for, could enhance model accuracy [5, 6, 8]. In this work, we revisit the problem ...

If it is to qualify as knowledge, a learner's output should be accurate, stable and comprehensible. Learning multiple models can improve signiicantly on the accuracy and stability of single models, but at the cost of losing their comprehensibility (when they possess it, as do, for example, simple decision trees and rule sets). This article proposes and evaluates CMM, a meta-learner that seeks t...