Index Catalog // ScholarsArchive@OSU

Creator:: Restificar, Angelo C., Dietterich, Thomas Glen, and Oregon State University. Department of Computer Science
Abstract:: An important challenge in machine learning is to find ways of learning quickly from very small amounts of training data. The only way to learn from small data samples is to constrain the learning process by exploiting background knowledge. In this report, we present a theoretical analysis on the use...
Resource Type:: Technical Report
Full Text:: Bayesian Network Learning Angelo C. Restificar Thomas G. Dietterich School of Electrical Engineering and

Creator:: Oregon State University. Dept. of Computer Science, Dietterich, Thomas Glen, and Langley, Pat
Abstract:: The field of machine learning has made major strides over the last 20 years. This document summarizes the major problem formulations that the discipline has studied, then reviews three tasks in cognitive networking and briefly discusses how aspects of those tasks fit these formulations. After this, it discusses challenges for...
Resource Type:: Research Paper
Full Text:: differential equations, and inferring conditional probabilities in a Bayesian network. Another variation, known

Creator:: Oregon State University. Dept. of Computer Science and Dietterich, Thomas Glen
Abstract:: What is the relationship between learning and reasoning? Much recent work in machine learning has been criticized for focusing on learning and ignoring reasoning. This paper attempts to describe the various ways in which machine learning research has (and has not) incorporated reasoning. The paper argues that there are important...
Resource Type:: Research Paper
Full Text:: represented by some form of Bayesian network. The simplest such network is the Naive Bayes network shown in

Creator:: Oregon State University. Dept. of Computer Science, Bayer-Zubek, Valentina, and Dietterich, Thomas Glen
Abstract:: This paper studies the problem of learning diagnostic policies from training examples. A diagnostic policy is a complete description of the decision-making actions of a diagnostician (i.e., tests followed by a diagnostic decision) for all possible combinations of test results. An optimal diagnostic policy is one that minimizes the expected...
Resource Type:: Research Paper
Full Text:: & Wessels, 1993; Madigan & Almond, 1996; Dittmer & Jensen, 1997), we do not assume a Bayesian network or

Creator:: Oregon State University. Dept. of Computer Science, Margineantu, Dragos D. (Dragos Dorin), and Dietterich, Thomas Glen
Abstract:: Many machine learning applications require classifiers that minimize an asymmetric cost function rather than the misclassification rate, and several recent papers have addressed this problem. However, these papers have either applied no statistical testing or have applied statistical methods that are not appropriate for the cost-sensitive setting. Without good statistical...
Resource Type:: Research Paper
Full Text:: Bayesian Methods. MIT Press, Cambridge, Mass. Knoll, U., Nakhaeizadeh, G., & Tausend, B. (1994). Cost

ScholarsArchive@OSU