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Filtering by: Creator Schmidt, Jerome Committee Member Fern, Alan

Efficient training and feature induction in sequential supervised learning

Creator:

Hao, Guohua

Abstract:

Sequential supervised learning problems arise in many real applications. This dissertation focuses on two important research directions in sequential supervised learning: efficient training and feature induction. In the direction of efficient training, we study the training of conditional random fields (CRFs), which provide a flexible and powerful model for sequential...

Resource Type:

Dissertation
Domain-Independent Planning for Markov Decision Processes with Factored State and Action Spaces

Creator:

Raghavan, Aswin

Abstract:

Markov Decision Processes (MDPs) are the de-facto formalism for studying sequential decision making problems with uncertainty, ranging from classical problems such as inventory control and path planning, to more complex problems such as reservoir control under rainfall uncertainty and emergency response optimization for fire and medical emergencies. Most prior research...

Resource Type:

Dissertation