We explore algorithms for learning classification procedures that attempt to minimize the cost of misclassifying examples. First, we consider inductive learning of classification ...
Michael J. Pazzani, Christopher J. Merz, Patrick M...
The development of natural language proccssing (NLP) systems that perform machine translation (MT) and information retrieval (IR) has highlighted the need for the automatic recogn...
Abstract. We consider an upper confidence bound algorithm for Markov decision processes (MDPs) with deterministic transitions. For this algorithm we derive upper bounds on the onl...
We study the problem of aggregating partial rankings. This problem is motivated by applications such as meta-searching and information retrieval, search engine spam fighting, e-c...