This paper examines how six online multiclass text classification algorithms perform in the domain of email tagging within the TaskTracer system. TaskTracer is a project-oriented user interface for the desktop knowledge worker. TaskTracer attempts to tag all documents, web pages, and email messages with the projects to which they are...
Coarse resolution imagery, such as that produced by the MODIS instrument, poses the challenge of estimating sub-pixel proportions of di erent land cover types. This problem is di cult because of the variety and variability of vegetation within individual pixels. This thesis describes and compares two existing algorithms for estimating...
The focus of this thesis involves developing general fabrication processes relevant to the manufacture of two new devices for the Microscale technology Energy and Chemical Systems (MECS) program at Oregon State University. The two MECS devices developed, capture dots and transparent thin-film heaters (TTFHs), require unique process development for successful...
This work presents a new energy saving technique for modern digital designs. We propose Time Interleaved Multi-Rail (TIMR) - a method for providing two dynamic supply rails to a circuit. This technique uses the first supply rail to mask the transition delay while changing the voltage of the second rail....
Increased interest in the field of sensor technology stems from the availability of an inexpensive and robust sensor to detect and quantify the presence of a specific gas. Bulk p-CuO/n-ZnO heterocontact based gas sensors have been shown to exhibit the necessary sensitivity and selectivity characteristics, however, low interfacial CuO/ZnO contact...
Many object recognition applications require detecting and responding to objects drawn from a different distribution from that of the training data. This task is referred to as out-of-distribution (OOD) detection, and it is often formulated as an outlier detection problem
wherein the probability distribution of the known data P(X) is...
Supervised learning is concerned with discovering the relationship between example sets of features and their corresponding classes. The traditional supervised learning formulation assumes that all examples are independent from one another. The order of the examples contains no information. Nonetheless, many problems have a sequential nature. Classifiers for these problems...
This thesis addresses the problem of learning dynamic Bayesian network (DBN) models to support reinforcement learning. It focuses on learning regression tree models of the conditional probability distributions of the DBNs. Existing algorithms presume that the stochasticity in the domain can be modeled as a deterministic function with additive noise....
Machine learning encompasses probabilistic and statistical techniques that can build models from large quantities of extensional information (examples) with minimal dependence on intensional information (domain knowledge). This focus of machine learning is reflected in the never-ending quest for "off-the-shelf" classifiers. To generalize to unseen data, however, we must make use...
Object categorization is one of the fundamental topics in computer vision research. Most current work in object categorization aims to discriminate among generic object classes with gross differences. However, many applications require much finer distinctions. This thesis focuses on the design, evaluation and analysis of learning algorithms for fine- grained...