straction is a useful tool for agents interacting with environments. Good state abstractions are compact, reuseable, and easy to learn from sample data. This paper and extends two...
This paper extends previous work on the Skewing algorithm, a promising approach that allows greedy decision tree induction algorithms to handle problematic functions such as parit...
We present a generative probabilistic model for the topographic mapping of tree structured data. The model is formulated as constrained mixture of hidden Markov tree models. A nat...
Discovering additive structure is an important step towards understanding a complex multi-dimensional function because it allows the function to be expressed as the sum of lower-d...
Standard Machine Learning approaches to text classification use the bag-of-words representation of documents to deceive the classification target function. Typical linguistic stru...