Results 1 
3 of
3
An analysis of Bayesian classifiers
 IN PROCEEDINGS OF THE TENTH NATIONAL CONFERENCE ON ARTI CIAL INTELLIGENCE
, 1992
"... In this paper we present anaveragecase analysis of the Bayesian classifier, a simple induction algorithm that fares remarkably well on many learning tasks. Our analysis assumes a monotone conjunctive target concept, and independent, noisefree Boolean attributes. We calculate the probability that t ..."
Abstract

Cited by 344 (17 self)
 Add to MetaCart
In this paper we present anaveragecase analysis of the Bayesian classifier, a simple induction algorithm that fares remarkably well on many learning tasks. Our analysis assumes a monotone conjunctive target concept, and independent, noisefree Boolean attributes. We calculate the probability that the algorithm will induce an arbitrary pair of concept descriptions and then use this to compute the probability of correct classification over the instance space. The analysis takes into account the number of training instances, the number of attributes, the distribution of these attributes, and the level of class noise. We also explore the behavioral implications of the analysis by presenting
Seer: Maximum Likelihood Regression for LearningSpeed Curves
 University of Illinois at
, 1995
"... The research presented here focuses on modeling machinelearning performance. The thesis introduces Seer, a system that generates empirical observations of classificationlearning performance and then uses those observations to create statistical models. The models can be used to predict the number ..."
Abstract

Cited by 10 (0 self)
 Add to MetaCart
The research presented here focuses on modeling machinelearning performance. The thesis introduces Seer, a system that generates empirical observations of classificationlearning 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 maximumlikelihood models, and 3) a demonstration on realworld 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 maximumlikelihood model of classificationlearning 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 learningperformance and that it seems to be as good as possible at predicting learning
Induction of OneLevel Decision Trees
 Proceedings of the Ninth International Conference on Machine Learning
, 1992
"... In recent years, researchers have made considerable progress on the worstcase analysis of inductive learning tasks, but for theoretical results to have impact on practice, they must deal with the average case. In this paper we present an averagecase analysis of a simple algorithm that induce ..."
Abstract

Cited by 2 (0 self)
 Add to MetaCart
In recent years, researchers have made considerable progress on the worstcase analysis of inductive learning tasks, but for theoretical results to have impact on practice, they must deal with the average case. In this paper we present an averagecase analysis of a simple algorithm that induces onelevel decision trees for concepts defined by a single relevant attribute. Given knowledge about the number of training instances, the number of irrelevant attributes, the amount of class and attribute noise, and the class and attribute distributions, we derive the expected classification accuracy over the entire instance space. We then examine the predictions of this analysis for different settings of these domain parameters, comparing them to experimental results to check our reasoning. 1