4.5k citations · 4.6k across the 7 of their papers we have counts for
8 papers
Quantifying the Value of Constructive Induction, Knowledge, and Noise Filtering on Inductive Learning
Carl M. Kadie
Learning research, as one of its central goals, tries to measure, model, and understand how learning-problem properties affect average-case learning performance. For example, we wo…
Seer: Maximum Likelihood Regression for Learning-Speed Curves
Carl Myers Kadie
The research presented here focuses on modeling machine-learning performance. The thesis introduces Seer, a system that generates empirical observations of classification-learning…
Rational Nonmonotonic Reasoning
Carl Kadie
Nonmonotonic reasoning is a pattern of reasoning that allows an agent to make and retract (tentative) conclusions from inconclusive evidence. This paper gives a possible-worlds int…
Empirical Analysis of Predictive Algorithms for Collaborative Filtering
John S. Breese, David Heckerman, Carl Kadie
Collaborative filtering or recommender systems use a database about user preferences to predict additional topics or products a new user might like. In this paper we describe sever…
Dependency Networks for Collaborative Filtering and Data Visualization
David Heckerman, David Maxwell Chickering, Christopher Meek +2
We describe a graphical model for probabilistic relationships---an alternative to the Bayesian network---called a dependency network. The graph of a dependency network, unlike a Ba…
CFW: A Collaborative Filtering System Using Posteriors Over Weights Of Evidence
Carl Kadie, Christopher Meek, David Heckerman
We describe CFW, a computationally efficient algorithm for collaborative filtering that uses posteriors over weights of evidence. In experiments on real data, we show that this met…