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...
Abstract. This paper presents an immune-inspired adaptable error detection (AED) framework for Automated Teller Machines (ATMs). This framework two levels, one level is local to a ...
When applying aggregating strategies to Prediction with Expert Advice, the learning rate must be adaptively tuned. The natural choice of complexity/current loss renders the analys...
We study the problem of learning to accurately rank a set of objects by combining a given collection of ranking or preference functions. This problem of combining preferences aris...
Yoav Freund, Raj D. Iyer, Robert E. Schapire, Yora...
Solving optimally large instances of combinatorial optimization problems requires a huge amount of computational resources. In this paper, we propose an adaptation of the parallel...