Friday Apr 28 A Constructive Learning Network Based on Nonparametric Regression: Receptive Field Locally Weighted Regression Stefan Schaal, Georgia Tech Methods from nonparametric statistics are the basis for one important class of learning algorithms in which the data is modeled by means of simple local functions. The local functions are either stored and a predicted value is generated by a blending of neighboring local functions, or the local functions are recalculated at the point where a prediction is required from the data in memory. The former approach is usually a computationally involved batch method and does not allow incremental learning; however its lookup speed is fast. The latter approach, in contrast, performs very fast incremental learning with minimal interference, but it requires a computationally expensive calculation at the moment a prediction is to be formed. Naturally the question arises whether an incremental nonparametric learning system can be accomplished which combines the best of both worlds. Receptive Field Locally Weighted Regression (RFLWR) is an attempt to achieve this goal. It represents the functional dependence of the mapping from input to output data by a sparse distributed code which is formed by a flexible number of local receptive fields. Each receptive field consists of an activation function and a locally linear output function. Parameters are adjusted incrementally by second order methods. In this way, each receptive field finds its own local distance metric, and it can cope with collinear data and nuisance dimensions. There is no competition among the receptive fields during learning and the size of the receptive fields reflects the functional relation of the input-output mapping and not -- as in several other receptive field methods -- merely the probability distribution of the input data. These properties lead to robustness towards dependent patterns of data sampling and avoid negative interference to a large extent. This talk will discuss the theoretical issues of RFLWR and demonstrate the aforementioned properties by means of synthetic, visualizable data sets.