This paper addresses the problem of detecting land-cover transitions by analysing multitemporal remote-sensing images. In order to develop an effective system for the detection of land-cover transitions, an ensemble of non-parametric multitemporal classifiers is defined and integrated in the context of a multiple classifier system (MCS). Each multitemporal classifier is developed in the framewo...

Extended Classifier Systems, or XCS, is a soft-computing approach to machine learning in rule-based systems. While XCS has been shown effective in learning accurate, compact and complete mappings of an environment’s payoff landscape, it can require significant resources to do so. This paper presents four modifications that allow XCS to achieve high performance even in highly size-constrained po...

S OF CURRENT LITERATURE The following Technical Reports cover work done under the DARPA research described on page 136. available from Department of Computer and Information Service University of Pennsylvania Philadelphia, PA 19104 Interactive Classification: A Technique for the Acquisition and Maintenance of Knowledge Bases Tim Finin, David Silverman MS-CIS-84-17 Reports are The practical appl...

A quantile binary classifier uses the rule: Classify x as +1 if P (Y = 1|X = x) ≥ τ , and as −1 otherwise, for a fixed quantile parameter τ ∈ [0, 1]. It has been shown that Support Vector Machines (SVMs) in the limit are quantile classifiers with τ = 12 . In this paper, we show that by using asymmetric cost of misclassification SVMs can be appropriately extended to recover, in the limit, the qu...

Genetic-based Learning Classifier Systems have been proposed as a competent technology for the classification of medical data sets. What is not known about this class of system is two­ fold. Firstly, how does a Learning Classifier System (LCS) perform when applied to the single-step classification of multiple-channel, noisy, artefact-inclusive human EEG signals acquired from many participants? ...