# Seer: Maximum Likelihood Regression for Learning-Speed Curves

**Carl M. Kadie**

**Microsoft Research, Bldg 9S**

**Redmond 98052-6399, WA**

**Author Email: ****carlk@microsoft.com**

### Abstract:

(Ph.D. Thesis, U. of Illinois. David C. Wilkins, Advisor)

*The research presented here focuses on modeling machine-learning
performance. The thesis introduces Seer, a system that generates
empirical observations of classification-learning performance
and then uses those observations to create statistical models.
The models can be used to predict the number of training examples
needed to achieve a desired level and the maximum accuracy possible
given an unlimited number of training examples. Seer advances
the state of the art with 1) models that embody the best constraints
for classification learning and most useful parameters, 2) algorithms
that efficiently find maximum-likelihood models, and 3) a demonstration
on real-world data from three domains of a practicable application
of such modeling.*

*The first part of the thesis gives an overview of the requirements
for a good maximum-likelihood model of classification-learning
performance. Next, reasonable design choices for such models are
explored. Selection among such models is a task of nonlinear programming,
but by exploiting appropriate problem constraints, the task is
reduced to a nonlinear regression task that can be solved with
an efficient iterative algorithm. The latter part of the thesis
describes almost 100 experiments in the domains of soybean disease,
heart disease, and audiological problems. The tests show that
Seer is excellent at characterizing learning-performance and that
it seems to be as good as possible at predicting learning performance.
Finally, recommendations for choosing a regression model for a
particular situation are made and directions for further research
are identified.*

Technical Report UIUCDCS-R-95-1874, Department of Computer Science,
University of Illinois, Urbana, IL, August 1995.

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