Skip to Main Content
Skip Nav Destination
ASME Press Select Proceedings

Intelligent Engineering Systems through Artificial Neural Networks, Volume 16

Editor
Cihan H. Dagli
Cihan H. Dagli
Search for other works by this author on:
Anna L. Buczak
Anna L. Buczak
Search for other works by this author on:
David L. Enke
David L. Enke
Search for other works by this author on:
Mark Embrechts
Mark Embrechts
Search for other works by this author on:
Okan Ersoy
Okan Ersoy
Search for other works by this author on:
ISBN-10:
0791802566
No. of Pages:
1000
Publisher:
ASME Press
Publication date:
2006

Many machine learning methods focus on the quality of prediction results as their final purpose. Spline kernel based methods attempt to provide also transparency to the prediction identifying features that are important in the decision process. In this paper, we present a new heuristic for computing efficiently sparse kernel in SUPANOVA. We applied it to a benchmark Boston housing market dataset and to socially important problem of improving the detection of heart diseases in the population using a novel, non-invasive measurement of the heart activities based on magnetic field produced by the human heart. On this data, 83.7% predictions were...

Abstract
Introduction
Supanova and Its Implementation
Performance Measurements and Data Benchmarks
Conclusions
Acknowledgement
References
This content is only available via PDF.
You do not currently have access to this chapter.
Close Modal

or Create an Account

Close Modal
Close Modal