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ASME Press Select Proceedings
Intelligent Engineering Systems through Artificial Neural Networks, Volume 16Available to Purchase
Editor
Cihan H. Dagli
Cihan H. Dagli
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Anna L. Buczak
Anna L. Buczak
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David L. Enke
David L. Enke
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Mark Embrechts
Mark Embrechts
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Okan Ersoy
Okan Ersoy
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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 correct, exceeding the results obtained using the standard Support Vector Machine and equivalent kernels. Equally good results were achieved by the spline kernel on a benchmark Boston housing market dataset.

Abstract
Introduction
Supanova and Its Implementation
Performance Measurements and Data Benchmarks
Conclusions
Acknowledgement
References
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