Learning and Example Selection for Object and Pattern Detection

This thesis presents a learning based approach for detecting classes of objects and patterns with variable image appearance but highly predictable image boundaries. It consists of two parts. In part one, we introduce our object and pattern detection approach using a concrete human face detection example. The approach first builds a distribution-based model of the target pattern class in an appropriate feature space to describe the target's variable image appearance. It then learns from examples a similarity measure for matching new patterns against the distribution-based target model. The approach makes few assumptions about the target pattern class and should therefore be fairly general, as long as the target class has predictable image boundaries. Because our object and pattern detection approach is very much learning-based, how well a system eventually performs depends heavily on the quality of training examples it receives. The second part of this thesis looks at how one can select high quality examples for function approximation learning tasks. We propose an {em active learning} formulation for function approximation, and show for three specific approximation function classes, that the active example selection strategy learns its target with fewer data samples than random sampling. We then simplify the original active learning formulation, and show how it leads to a tractable example selection paradigm, suitable for use in many object and pattern detection problems.

Logging and Recovery in a Highly Concurrent Database

This report addresses the problem of fault tolerance to system failures for database systems that are to run on highly concurrent computers. It assumes that, in general, an application may have a wide distribution in the lifetimes of its transactions. Logging remains the method of choice for ensuring fault tolerance. Generational garbage collection techniques manage the limited disk space reserved for log information; this technique does not require periodic checkpoints and is well suited for applications with a broad range of transaction lifetimes. An arbitrarily large collection of parallel log streams provide the necessary disk bandwidth.