Index Catalog // ScholarsArchive@OSU

Machine Learning Methods for Computational Sustainability

Creator:: Liu, Liping
Abstract:: Maintaining the sustainability of the earth’s ecosystems has attracted much attention as these ecosystems are facing more and more pressure from human activities. Machine learning can play an important role in promoting sustainability as a large amount of data is being collected from ecosystems. There are at least three important...
Resource Type:: Dissertation
Full Text:: AN ABSTRACT OF THE DISSERTATION OF Liping Liu for the

Anomaly Detection: Theory, Explanation and User Feedback

Creator:: Siddiqui, Md Amran
Abstract:: Anomaly detection has been used in variety of applications in practice, including cyber-security, fraud detection and detecting faults in safety critical systems, etc. Anomaly detectors produce a ranked list of statistical anomalies, which are typically examined by human analysts in order to extract the actual anomalies of interest. Unfortunately, most...
Resource Type:: Dissertation
Full Text:: form of true/false positive feedback from the analyst. We show empirically the efficacy and the

Combining coordination mechanisms to improve performance in multi-robot teams

Creator:: Nasroullahi, Ehsan
Abstract:: Coordination is essential to achieving good performance in cooperative multiagent systems. To date, most work has focused on either implicit or explicit coordination mechanisms, while relatively little work has focused on the benefits of combining these two approaches. In this work we demonstrate that combining explicit and implicit mechanisms can...
Resource Type:: Masters Thesis
Full Text:: combine these two approaches on a vari- ant of a team-based multi-robot exploration domain, and show

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:: together, the results of our experiments show that supporting rich interactions between users and machine

Robust and Efficient Classification of Videos in the Wild

Creator:: Mahasseni, Behrooz
Abstract:: Recognizing human actions in videos is a long-standing problem in computer vision with a wide range of applications including video surveillance, content retrieval, and sports analysis. This thesis focuses on addressing efficiency and robustness of video classification in unconstrained real-world settings. The thesis work can be broadly divided into four...
Resource Type:: Dissertation
Full Text:: show that we outperform standard multitask learning by 21.9%, and the state-of-the-art alternatives by

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:: heavily on system communication in order to respond to changes. The results show that in order to

Improving automated email tagging with implicit feedback

Creator:: Sorower, Mohammad Shahed
Abstract:: Machine learning systems are generally trained offline using ground truth data that has been labeled by experts. However, these batch training methods are not a good fit for many applications, especially in the cases where complete ground truth data is not available for offline training. In addition, batch methods do...
Resource Type:: Dissertation
Full Text:: manage their own email messages. The results show that implicit feedback produces important increases in

Rare category detection using hierarchical mean shift

Creator:: Vatturi, Pavan Kumar
Abstract:: Many applications in surveillance, monitoring, scientific discovery, and data cleaning require the identification of anomalies. Although many methods have been developed to identify statistically significant anomalies, a more difficult task is to identify anomalies that are both interesting and statistically significant. Category detection is an emerging area of machine learning...
Resource Type:: Masters Thesis
Full Text:: classes. Results on real-world data sets show that our hierarchical mean shift approach performs

Lamb Wave Mode Decomposition and Its Applications in Structural Health Monitoring

Creator:: Zoubi, Ahmad Bassil
Abstract:: Structural health monitoring (SHM) systems perform automated non-destructive damage detection and characterization for a variety of large structures including civil structures such as bridges and aerospace structures such as aircrafts and space vehicles. The goals of SHM include preventing catastrophic structural failures, increasing reliability, reducing maintenance costs, and increasing the...
Resource Type:: Dissertation
Full Text:: -scan, are drawn in blue. The black dots show sensor locations on the structure. The experiment

Active learning with committees : an approach to efficient learning in text categorization using linear threshold algorithms

Creator:: Liere, Ray
Abstract:: We developed and investigated machine learning methods that require minimal preprocessing of the input data, use few training examples, run fast, and still obtain high levels of accuracy. Most approaches to designing machine learning programs are based on the supervised learning paradigm – training examples are chosen randomly and given...
Resource Type:: Dissertation
Full Text:: systems. Our results show that it is possible to design active learning systems that scale up to large

ScholarsArchive@OSU

Machine Learning Methods for Computational Sustainability

Anomaly Detection: Theory, Explanation and User Feedback

Combining coordination mechanisms to improve performance in multi-robot teams

Interacting meaningfully with machine learning systems : three experiments

Robust and Efficient Classification of Videos in the Wild

Integrating rich user feedback into intelligent user interfaces

Improving automated email tagging with implicit feedback

Rare category detection using hierarchical mean shift

Lamb Wave Mode Decomposition and Its Applications in Structural Health Monitoring

Active learning with committees : an approach to efficient learning in text categorization using linear threshold algorithms

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