We prove the strongest known bound for the risk of hypotheses selected from the ensemble generated by running a learning algorithm incrementally on the training data. Our result i...
Constructing the face of a criminal from the selection of individual facial parts is a hard task. We have been working on a new system called EvoFIT that involves the selection an...
Charlie D. Frowd, Joanne Park, Alex H. McIntyre, V...
We explore the problem of budgeted machine learning, in which the learning algorithm has free access to the training examples’ labels but has to pay for each attribute that is s...
Kun Deng, Chris Bourke, Stephen D. Scott, Julie Su...
In document retrieval using pseudo relevance feedback, after initial ranking, a fixed number of top-ranked documents are selected as feedback to build a new expansion query model. ...
Abstract. We study predicate selection functions (also known as splitting rules) for structural decision trees and propose two improvements to existing schemes. The first is in cl...