A basic tradeoff to consider when designing a distributed data-mining framework is the need for a compromise between the cost of communication and computation resources and the accuracy of the mining results. This is es ...

Within the past several years the technology of high-throughput sequencing has transformed the study of biology by offering unprecedented access to life's fundamental building block, DNA. With this transformation's poten ...

Motion capture data is a digital representation of the complex temporal structure of human motion. Motion capture is widely used for data-driven animation in sports,medicine and entertainment, because of its ability to c ...

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 t ...

Intelligent user interfaces, such as recommender systems and email classifiers, use machine learning algorithms to customize their behavior to the preferences of an end user. Although these learning systems are somewhat ...

Full dynamic control of physically simulated characters is the holy grail of video games and other interactive applications. Recent advances in the field have presented controllers that can handle the balance and fall ...

The potential for machine learning systems to improve via a mutually beneficial exchange of information with users has yet to be explored in much detail. Previously, we found that users were willing to provide a generous ...

The term Citizen Science refers to scientific research in which volunteers from the community participate in scientific studies as field assistants. Since citizen scientists can collect data cheaply, they enable research ...

This thesis presents a progression of novel planning algorithms that culminates in a new family of diverse Monte-Carlo methods for probabilistic planning domains. We provide a proof for performance guarantees and analyz ...

In text classification, labeling features is often less time consuming than labeling entire documents. In situations where very little labeled training data is available, feature relevance feedback has the potential to d ...