We consider the problem of learning to predict as well as the best in a group of experts making continuous predictions. We assume the learning algorithm has prior knowledge of the maximum number of mistakes of the best expert. We propose a new master strategy that achieves the best known performance for online learning with continuous experts in the mistake bounded model. Our ideas are based on...

In this paper, we derive a novel probabilistic model of boosting as a Product of Experts. We re-derive the boosting algorithm as a greedy incremental model selection procedure which ensures that addition of new experts to the ensemble does not decrease the likelihood of the data. These learning rules lead to a generic boosting algorithm POEBoost which turns out to be similar to the AdaBoost alg...

We consider the problem of selecting the articles of experts' interest from a literature database with the assistance of a machine learning system. For this purpose, we propose the rough reading strategy which combines the experts' knowledge with the machine learning system. For the articles converted through the rough reading strategy, we employ the learning system BONSAI and apply it for disc...

T improve operational efficiencies while providing state of the art healthcare services, hospitals rely on information technology enabled physician referral systems (IT-PRS). This study examines learning curves in an IT-PRS setting to determine whether agents achieve performance improvements from cumulative experience at different rates and how information technologies transform the learning dy...

The affordance of online communication and collaboration technology provides a forum for preservice teachers, practicing teacher educators to engage in authentic discourse where multiple perspectives can be shared. This qualitative case study explores the perceptions experiences embedded experts global learning community that occurred over 12 year period. was designed using Online Collaborative...

We present a multiagent learning algorithm with guaranteed convergence to Nash equilibria for all games. Our approach is a regret-based learning algorithm which combines a greedy random sampling method with consultation of experts that suggest possible strategy profiles. More importantly, by consulting carefully chosen experts we can greatly improve the convergence rate to Nash equilibria, but ...

We present products of hidden Markov models (PoHMM's), a way of combining HMM's to form a distributed state time series model. Inference in a PoHMM is tractable and eÆcient. Learning of the parameters, although intractable, can be e ectively done using the Product of Experts learning rule. The distributed state helps the model to explain data which has multiple causes, and the fact that each mo...