Machine learning is typically considered a graduate-level course with an artificial intelligence course as a prerequisite. However, it does not need to be positioned this way, and...
The APE (Adaptive Programming Environment) project focuses on applying Machine Learning techniques to embed a software assistant into the VisualWorks Smalltalk interactive program...
We describe a user interface for wireless information devices, specifically designed to facilitate learning about users’ individual interests in daily news stories. User feedbac...
We propose a new rule induction algorithm for solving classification problems via probability estimation. The main advantage of decision rules is their simplicity and good interp...
We present feature transformations useful for exploratory data analysis or for pattern recognition. Transformations are learned from example data sets by maximizing the mutual inf...
Cognitive modeling with neural networks unrealistically ignores the role of knowledge in learning by starting from random weights. It is likely that effective use of knowledge by ...
The reinforcement learning problem can be decomposed into two parallel types of inference: (i) estimating the parameters of a model for the underlying process; (ii) determining be...
In this paper, we describe our research in computer-aided image analysis. We have incorporated machine learning methodologies with traditional image processing to perform unsuperv...
This paper considers approaches which rerank the output of an existing probabilistic parser. The base parser produces a set of candidate parses for each input sentence, with assoc...