Conference proceeding
An Inductive Learning Approach to Prognostic Prediction
Machine Learning Proceedings 1995, pp.522-530
1995
DOI: 10.1016/B978-1-55860-377-6.50071-2
Abstract
This paper introduces the Recurrence Surface Approximation, an inductive learning method based on linear programming that predicts recurrence times using censored training examples, that is, examples in which the available training output may be only a lower bound on the “right answer.” This approach is augmented with a feature selection method that chooses an appropriate feature set within the context of the linear programming generalizer. Computational results in the field of breast cancer prognosis are shown. A straightforward translation of the prediction method to an artificial neural network model is also proposed.
Details
- Title: Subtitle
- An Inductive Learning Approach to Prognostic Prediction
- Creators
- W. Nick Street - University of Wisconsin–MadisonO.L. Mangasarian - University of Wisconsin–MadisonW.H. Wolberg - University of Wisconsin–Madison
- Resource Type
- Conference proceeding
- Publication Details
- Machine Learning Proceedings 1995, pp.522-530
- Publisher
- Elsevier Inc
- DOI
- 10.1016/B978-1-55860-377-6.50071-2
- Language
- English
- Date published
- 1995
- Academic Unit
- Nursing; Computer Science; Business Analytics; Bus Admin College
- Record Identifier
- 9984380729302771
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