Connectionist learning of expert preferences by comparison training
Advances in neural information processing systems 1
Self-Organizing Maps
Active Exploration in Instance-Based Preference Modeling
ICCBR '99 Proceedings of the Third International Conference on Case-Based Reasoning and Development
Self-Organizing Cases to Find Paradigms
IWANN '99 Proceedings of the International Work-Conference on Artificial and Natural Neural Networks: Foundations and Tools for Neural Modeling
A system for induction of oblique decision trees
Journal of Artificial Intelligence Research
Journal of Artificial Intelligence Research
Feature subset selection for learning preferences: a case study
ICML '04 Proceedings of the twenty-first international conference on Machine learning
| Hi-index | 0.00 |
In this paper we present an algorithm for learning a function able to assess objects. We assume that our teachers can provide a collection of pairwise comparisons but encounter certain difficulties in assigning a number to the qualities of the objects considered. This is a typical situation when dealing with food products, where it is very interesting to have repeatable, reliable mechanisms that are as objective as possible to evaluate quality in order to provide markets with products of a uniform quality. The same problem arises when we are trying to learn user preferences in an information retrieval system or in configuring a complex device. The algorithm is implemented using a growing variant of Kohonen's Self-Organizing Maps (growing neural gas), and is tested with a variety of data sets to demonstrate the capabilities of our approach.