this paper extends the sequential learning algorithm strategy of two different types of adaptive radial basis function-based (rbf) neural networks, i.e. growing and pruning radial basis function (gap-rbf) and minimal resource allocation network (mran) to cater for on-line identification of non-linear systems. the original sequential learning algorithm is based on the repetitive utilization of s...

In this paper we propose a three-stage incremental approach to the development of autonomous agents. We discuss some issues about the characteristics which differentiate reinforcement programs (RPs), and define the trainer as a particular kind of RP. We present a set of results obtained running experiments with a trainer which provides guidance to the AutonoMouse, our mouse-sized autonomous rob...

Topological phase transitions, which do not adhere to Landau's phenomenological model (i.e., a spontaneous symmetry breaking process and vanishing local order parameters), have been actively researched in condensed matter physics. Machine learning of topological transitions has generally proved difficult due the global nature indices. Only recently method diffusion maps shown be effective at id...

Phase transitions typically occur in combinatorial computational problems and have important consequences, especially with the current spread of statistical relational learning and of sequence learning methodologies. In Phase Transitions in Machine Learning the authors begin by describing in detail this phenomenon and the extensive experimental investigation that supports its presence. They the...

We study generalization in a fully connected two-layer neural network with multiple output nodes. Similarly to the learning of fully connected committee machine, the learning is characterized by a discontinuous phase transition between the permutation symmetric phase and the permutation symmetry breaking phase. We nd that the learning curve in the permutation symmetric phase is universal, irres...