Published August 20, 2002
| public
Book Section - Chapter
The Multilevel Classification Problem and a Monotonicity Hint
Abstract
We introduce and formalize the multilevel classification problem, in which each category can be subdivided into different levels. We analyze the framework in a Bayesian setting using Normal class conditional densities. Within this framework, a natural monotonicity hint converts the problem into a nonlinear programming task, with non-linear constraints. We present Monte Carlo and gradient based techniques for addressing this task, and show the results of simulations. Incorporation of monotonicity yields a systematic improvement in performance.
Additional Information
© 2002 Springer-Verlag Berlin Heidelberg. First Online 20 August 2002. Many have contributed to the progress of this work. In particular, we single out Honeywell Corporation for alerting us to the problem and providing initial motivation, James Psota and Amir Atiya for useful discussion.Additional details
- Eprint ID
- 96896
- Resolver ID
- CaltechAUTHORS:20190702-152246968
- Created
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2019-07-08Created from EPrint's datestamp field
- Updated
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2021-11-16Created from EPrint's last_modified field
- Series Name
- Lecture Notes in Computer Science
- Series Volume or Issue Number
- 2412