Index Catalog // ScholarsArchive@OSU

Machine learning in engineering : techniques to speed up numerical optimization

Creator:: Cerbone, G. (Giuseppe)
Abstract:: Many important application problems in engineering can be formalized as nonlinear optimization tasks. However, numerical methods for solving such problems are brittle and do not scale well. For example, these methods depend critically on choosing a good starting point from which to perform the optimization search. In high-dimensional spaces, numerical...
Resource Type:: Dissertation
Full Text:: Machine learning in engineering : techniques to speed up

Automated Assembly Planning : From CAD Model to Virtual Assembly Process

Creator:: Rafibakhsh, Nima
Abstract:: Assembly planning is a crucial task for every manufacturing product. In general, assembly operations consume more than 30% of the total manufacturing time and cost. Therefore, any effort in optimizing assembly will have a significant impact on the economic success of manufacturing. Finding an optimal assembly plan by hand is...
Resource Type:: Dissertation
Full Text:: Rafibakhsh for the degree of Doctor of Philosophy in Mechanical Engineering presented on March 21, 2017

Learning and reasoning

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:: ) incorporated reasoning. The paper argues that there are important computational, statistical, and engineering

Learning diagnostic policies from examples by systematic search

Creator:: Oregon State University. Dept. of Computer Science and Zubek, Valentina Bayer
Abstract:: A diagnostic policy species what test to perform next based on the results of previous tests and when to stop and make a diagnosis. Cost-sensitive diagnostic policies perform tradeoffs between (a) the costs of tests and (b) the costs of misdiagnoses. An optimal diagnostic policy minimizes the expected total cost....
Resource Type:: Research Paper
Full Text:: Systematic Search Valentina Bayer�Zubek School of Electrical Engineering and Computer Science Oregon State

Toward harnessing user feedback for machine learning

Creator:: Stumpf, Simone, Rajaram, Vidya, Li, Lida, Burnett, Margaret, 1949-, Dietterich, Thomas Glen, Sullivan, Erin, Drummond, Russell, and Herlocker, Jonathan Lee
Abstract:: There has been little research into how end users might be able to communicate advice to machine learning systems. If this resource--the users themselves--could somehow work hand-in-hand with machine learning systems, the accuracy of learning systems could be improved and the users' understanding and trust of the system could improve...
Resource Type:: Technical Report
Full Text:: Drummond, Jonathan Herlocker Oregon State University School of Electrical Engineering and Computer

Interacting meaningfully with machine learning systems : three experiments

Creator:: Oregon State University. Dept. of Computer Science, Stumpf, Simone, Rajaram, Vidya, Li, Lida, Wong, Weng-Keen, Burnett, Margaret, 1949-, Dietterich, Thomas Glen, Sullivan, Erin, and Herlocker, Jonathan Lee
Abstract:: Although machine learning is becoming commonly used in today's software, there has been little research into how end users might interact with machine learning systems, beyond communicating simple "right/wrong" judgments. If the users themselves could somehow work hand-in-hand with machine learning systems, the accuracy of learning systems could be improved...
Resource Type:: Research Paper
Full Text:: Oregon State University School of Electrical Engineering and Computer Science Corvallis, OR 97331 USA

Machine learning for cognitive networks : technology assessment and research challenges

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:: mattered greatly, and careful feature engineering remains a hallmark of successful applications of machine

Machine learning for activity recognition

Creator:: Oregon State University. Dept. of Computer Science and Shen, Jianqiang
Abstract:: This paper surveys the activity recognition task from a machine learning perspective. I give a definition of this problem, and I classify different activity recognition problems into two categories. I show the activities can be hierarchical, and based on such hierarchies I synthesize a language to describe activities. I give...
Resource Type:: Research Paper
Full Text:: learning applications can be traced to careful engineering of the input features [18]. By designing

Integrating rich user feedback into intelligent user interfaces

Creator:: Oregon State University. Dept. of Computer Science, Stumpf, Simone, Sullivan, Erin, Fitzhenry, Erin, Oberst, Ian, Wong, Weng-Keen, and Burnett, Margaret, 1949-
Abstract:: 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 amount of rich feedback to machine learning systems, and that the types of some...
Resource Type:: Research Paper
Full Text:: dropdown menu in the feedback panel). Once changes were applied, the program updated the pre- dicted

Exploiting monotonicity via logistic regression in Bayesian network learning

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

ScholarsArchive@OSU

Machine learning in engineering : techniques to speed up numerical optimization

Automated Assembly Planning : From CAD Model to Virtual Assembly Process

Learning and reasoning

Learning diagnostic policies from examples by systematic search

Toward harnessing user feedback for machine learning

Interacting meaningfully with machine learning systems : three experiments

Machine learning for cognitive networks : technology assessment and research challenges

Machine learning for activity recognition

Integrating rich user feedback into intelligent user interfaces

Exploiting monotonicity via logistic regression in Bayesian network learning

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