Meta-Learning has been successfully applied to acquire knowledge used to support the selection of learning algorithms. Each training example in Meta-Learning (i.e. each meta-exampl...
In many applications in mobile robotics, it is important for a robot to explore its environment in order to construct a representation of space useful for guiding movement. We refe...
This contribution develops a theoretical framework that takes into account the effect of approximate optimization on learning algorithms. The analysis shows distinct tradeoffs for...
Artificial Neural Networks for online learning problems are often implemented with synaptic plasticity to achieve adaptive behaviour. A common problem is that the overall learning...
Machine learning and data mining have become aware that using constraints when learning patterns and rules can be very useful. To this end, a large number of special purpose syste...
In this paper, Multi-View Expectation and Maximization algorithm for finite mixture models is proposed by us to handle realworld learning problems which have natural feature split...
This paper investigates a class of learning problems called learning satisfiability (LSAT) problems, where the goal is to learn a set in the input (feature) space that satisfies...
Frederic Thouin, Mark Coates, Brian Eriksson, Robe...
Inductive Logic Programming (ILP) [1] systems are general purpose learners that have had significant success on solving a number of relational problems, particularly from the biol...