Sonar: Sonar, Mines vs. Rocks
Description
This is the data set used by Gorman and Sejnowski in their
study of the classification of sonar signals using a neural network
[1]. The task is to train a network to discriminate between sonar
signals bounced off a metal cylinder and those bounced off a roughly
cylindrical rock.
Each pattern is a set of 60 numbers in the range 0.0 to 1.0. Each
number represents the energy within a particular frequency band,
integrated over a certain period of time. The integration aperture
for higher frequencies occur later in time, since these frequencies
are transmitted later during the chirp.
The label associated with each record contains the letter "R" if the
object is a rock and "M" if it is a mine (metal cylinder). The
numbers in the labels are in increasing order of aspect angle, but
they do not encode the angle directly.format
A data frame with 208 observations on 61 variables, all numerical and one (the Class) nominal.source
- Contribution: Terry Sejnowski, Salk Institute and
University of California, San Deigo.
- Development: R. Paul Gorman, Allied-Signal Aerospace
Technology Center.
- Maintainer: Scott E. Fahlman
These data have been taken from the UCI Repository Of Machine Learning
Databases at
- ftp://ftp.ics.uci.edu/pub/machine-learning-databases
- http://www.ics.uci.edu/~mlearn/MLRepository.html
and were converted to R format by Evgenia.Dimitriadou@ci.tuwien.ac.at.References
1. Gorman, R. P., and Sejnowski, T. J. (1988). "Analysis of Hidden
Units in a Layered Network Trained to Classify Sonar Targets" in
Neural Networks, Vol. 1, pp. 75-89.