From continuous behaviour to discrete knowledge

0Citations
Citations of this article
3Readers
Mendeley users who have this article in their library.
Get full text

Abstract

Neural networks have proven to be very powerful techniques for solving a wide range of tasks. However, the learned concepts are unreadable for humans. Some works try to obtain symbolic models from the networks, once these networks have been trained, allowing to understand the model by means of decision trees or rules that are closer to human understanding. The main problem of this approach is that neural networks output a continuous range of values, so even though a symbolic technique could be used to work with continuous classes, this output would still be hard to understand for humans. In this work, we present a system that is able to model a neural network behaviour by discretizing its outputs with a vector quantization approach, allowing to apply the symbolic method. © Springer-Verlag Berlin Heidelberg 2003.

Cite

CITATION STYLE

APA

Ledezma, A., Fernández, F., & Aler, R. (2003). From continuous behaviour to discrete knowledge. Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2687, 217–224. https://doi.org/10.1007/3-540-44869-1_28

Register to see more suggestions

Mendeley helps you to discover research relevant for your work.

Already have an account?

Save time finding and organizing research with Mendeley

Sign up for free