We study cost-sensitive learning of decision trees that incorporate both test costs and misclassification costs. In particular, we first propose a lazy decision tree learning that ...
Io is small prototype-based programming language. The ideas in Io are mostly inspired by Smalltalk[1] (all values are objects), Self[2] (prototype-based), NewtonScript[3] (differe...
Recent work is beginning to reveal the potential of numerical optimization as an approach to generating interfaces and displays. Optimization-based approaches can often allow a mi...
We consider the problem of evaluating a large number of XPath expressions on an XML stream. Our main contribution consists in showing that Deterministic Finite Automata (DFA) can b...
Todd J. Green, Gerome Miklau, Makoto Onizuka, Dan ...
This paper introduces a Bayesian algorithm for constructing predictive models from data that are optimized to predict a target variable well for a particular instance. This algori...