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You searched for +publisher:"Oregon State University" +contributor:("Wong, Weng-Keen"). Showing records 1 – 15 of 15 total matches.

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Oregon State University

1. Singh, Satpreet Harcharan. Visualization and Analysis of Sensor Data for Detecting Microclimate Cold Air Pools.

Degree: MS, 2017, Oregon State University

 Cold air pools are spatiotemporal phenomena that occur when cold air from higher elevations roll down the slope to accumulate in lower elevations. Behaviors like… (more)

Subjects/Keywords: Cold Air Pools

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APA (6th Edition):

Singh, S. H. (2017). Visualization and Analysis of Sensor Data for Detecting Microclimate Cold Air Pools. (Masters Thesis). Oregon State University. Retrieved from http://hdl.handle.net/1957/61773

Chicago Manual of Style (16th Edition):

Singh, Satpreet Harcharan. “Visualization and Analysis of Sensor Data for Detecting Microclimate Cold Air Pools.” 2017. Masters Thesis, Oregon State University. Accessed April 26, 2019. http://hdl.handle.net/1957/61773.

MLA Handbook (7th Edition):

Singh, Satpreet Harcharan. “Visualization and Analysis of Sensor Data for Detecting Microclimate Cold Air Pools.” 2017. Web. 26 Apr 2019.

Vancouver:

Singh SH. Visualization and Analysis of Sensor Data for Detecting Microclimate Cold Air Pools. [Internet] [Masters thesis]. Oregon State University; 2017. [cited 2019 Apr 26]. Available from: http://hdl.handle.net/1957/61773.

Council of Science Editors:

Singh SH. Visualization and Analysis of Sensor Data for Detecting Microclimate Cold Air Pools. [Masters Thesis]. Oregon State University; 2017. Available from: http://hdl.handle.net/1957/61773


Oregon State University

2. Maniar, Dharin Haresh. Classification of motion capture sequences.

Degree: MS, Computer Science, 2008, Oregon State University

 Motion capture data is a digital representation of the complex temporal structure of human motion. Motion capture is widely used for data-driven animation in sports,medicine… (more)

Subjects/Keywords: Conditional Random fields; Motion  – Computer simulation

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APA (6th Edition):

Maniar, D. H. (2008). Classification of motion capture sequences. (Masters Thesis). Oregon State University. Retrieved from http://hdl.handle.net/1957/10015

Chicago Manual of Style (16th Edition):

Maniar, Dharin Haresh. “Classification of motion capture sequences.” 2008. Masters Thesis, Oregon State University. Accessed April 26, 2019. http://hdl.handle.net/1957/10015.

MLA Handbook (7th Edition):

Maniar, Dharin Haresh. “Classification of motion capture sequences.” 2008. Web. 26 Apr 2019.

Vancouver:

Maniar DH. Classification of motion capture sequences. [Internet] [Masters thesis]. Oregon State University; 2008. [cited 2019 Apr 26]. Available from: http://hdl.handle.net/1957/10015.

Council of Science Editors:

Maniar DH. Classification of motion capture sequences. [Masters Thesis]. Oregon State University; 2008. Available from: http://hdl.handle.net/1957/10015


Oregon State University

3. Kulesza, Todd. Toward end-user debugging of machine-learned programs.

Degree: MS, Computer Science, 2009, Oregon State University

 The results of a machine learning from user behavior can be thought of as a program, and like all programs, it may need to be… (more)

Subjects/Keywords: debugging; End-user computing

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APA (6th Edition):

Kulesza, T. (2009). Toward end-user debugging of machine-learned programs. (Masters Thesis). Oregon State University. Retrieved from http://hdl.handle.net/1957/13707

Chicago Manual of Style (16th Edition):

Kulesza, Todd. “Toward end-user debugging of machine-learned programs.” 2009. Masters Thesis, Oregon State University. Accessed April 26, 2019. http://hdl.handle.net/1957/13707.

MLA Handbook (7th Edition):

Kulesza, Todd. “Toward end-user debugging of machine-learned programs.” 2009. Web. 26 Apr 2019.

Vancouver:

Kulesza T. Toward end-user debugging of machine-learned programs. [Internet] [Masters thesis]. Oregon State University; 2009. [cited 2019 Apr 26]. Available from: http://hdl.handle.net/1957/13707.

Council of Science Editors:

Kulesza T. Toward end-user debugging of machine-learned programs. [Masters Thesis]. Oregon State University; 2009. Available from: http://hdl.handle.net/1957/13707


Oregon State University

4. Vatturi, Pavan Kumar. Rare category detection using hierarchical mean shift.

Degree: MS, Computer Science, 2009, Oregon State University

 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… (more)

Subjects/Keywords: machine learning; Machine learning

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APA (6th Edition):

Vatturi, P. K. (2009). Rare category detection using hierarchical mean shift. (Masters Thesis). Oregon State University. Retrieved from http://hdl.handle.net/1957/10191

Chicago Manual of Style (16th Edition):

Vatturi, Pavan Kumar. “Rare category detection using hierarchical mean shift.” 2009. Masters Thesis, Oregon State University. Accessed April 26, 2019. http://hdl.handle.net/1957/10191.

MLA Handbook (7th Edition):

Vatturi, Pavan Kumar. “Rare category detection using hierarchical mean shift.” 2009. Web. 26 Apr 2019.

Vancouver:

Vatturi PK. Rare category detection using hierarchical mean shift. [Internet] [Masters thesis]. Oregon State University; 2009. [cited 2019 Apr 26]. Available from: http://hdl.handle.net/1957/10191.

Council of Science Editors:

Vatturi PK. Rare category detection using hierarchical mean shift. [Masters Thesis]. Oregon State University; 2009. Available from: http://hdl.handle.net/1957/10191


Oregon State University

5. Oberst, Ian. On feature relevance feedback methods : incorporating labeled user features.

Degree: MS, Computer Science, 2010, Oregon State University

 In text classification, labeling features is often less time consuming than labeling entire documents. In situations where very little labeled training data is available, feature… (more)

Subjects/Keywords: feature relevance feedback; Information retrieval

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APA (6th Edition):

Oberst, I. (2010). On feature relevance feedback methods : incorporating labeled user features. (Masters Thesis). Oregon State University. Retrieved from http://hdl.handle.net/1957/17411

Chicago Manual of Style (16th Edition):

Oberst, Ian. “On feature relevance feedback methods : incorporating labeled user features.” 2010. Masters Thesis, Oregon State University. Accessed April 26, 2019. http://hdl.handle.net/1957/17411.

MLA Handbook (7th Edition):

Oberst, Ian. “On feature relevance feedback methods : incorporating labeled user features.” 2010. Web. 26 Apr 2019.

Vancouver:

Oberst I. On feature relevance feedback methods : incorporating labeled user features. [Internet] [Masters thesis]. Oregon State University; 2010. [cited 2019 Apr 26]. Available from: http://hdl.handle.net/1957/17411.

Council of Science Editors:

Oberst I. On feature relevance feedback methods : incorporating labeled user features. [Masters Thesis]. Oregon State University; 2010. Available from: http://hdl.handle.net/1957/17411


Oregon State University

6. Das, Shubhomoy. Incorporating User Feedback into Machine Learning Systems.

Degree: PhD, 2017, Oregon State University

 Although machine learning systems are often effective in real-world applications, there are situations in which they can be even better when provided with some degree… (more)

Subjects/Keywords: Machine Learning

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APA (6th Edition):

Das, S. (2017). Incorporating User Feedback into Machine Learning Systems. (Doctoral Dissertation). Oregon State University. Retrieved from http://hdl.handle.net/1957/61580

Chicago Manual of Style (16th Edition):

Das, Shubhomoy. “Incorporating User Feedback into Machine Learning Systems.” 2017. Doctoral Dissertation, Oregon State University. Accessed April 26, 2019. http://hdl.handle.net/1957/61580.

MLA Handbook (7th Edition):

Das, Shubhomoy. “Incorporating User Feedback into Machine Learning Systems.” 2017. Web. 26 Apr 2019.

Vancouver:

Das S. Incorporating User Feedback into Machine Learning Systems. [Internet] [Doctoral dissertation]. Oregon State University; 2017. [cited 2019 Apr 26]. Available from: http://hdl.handle.net/1957/61580.

Council of Science Editors:

Das S. Incorporating User Feedback into Machine Learning Systems. [Doctoral Dissertation]. Oregon State University; 2017. Available from: http://hdl.handle.net/1957/61580

7. Zheng, Yonglei. Predicting activity type from accelerometer data.

Degree: MS, Computer Science, 2012, Oregon State University

 The study of physical activity is important in improving people’s health as it can help people understand the relationship between physical activity and health. Accelerometers,… (more)

Subjects/Keywords: Accelerometers  – Data processing

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APA (6th Edition):

Zheng, Y. (2012). Predicting activity type from accelerometer data. (Masters Thesis). Oregon State University. Retrieved from http://hdl.handle.net/1957/33727

Chicago Manual of Style (16th Edition):

Zheng, Yonglei. “Predicting activity type from accelerometer data.” 2012. Masters Thesis, Oregon State University. Accessed April 26, 2019. http://hdl.handle.net/1957/33727.

MLA Handbook (7th Edition):

Zheng, Yonglei. “Predicting activity type from accelerometer data.” 2012. Web. 26 Apr 2019.

Vancouver:

Zheng Y. Predicting activity type from accelerometer data. [Internet] [Masters thesis]. Oregon State University; 2012. [cited 2019 Apr 26]. Available from: http://hdl.handle.net/1957/33727.

Council of Science Editors:

Zheng Y. Predicting activity type from accelerometer data. [Masters Thesis]. Oregon State University; 2012. Available from: http://hdl.handle.net/1957/33727

8. Kulesza, Todd. Personalizing machine learning systems with explanatory debugging.

Degree: PhD, Computer Science, 2014, Oregon State University

 How can end users efficiently influence the predictions that machine learning systems make on their behalf? Traditional systems rely on users to provide examples of… (more)

Subjects/Keywords: machine learning; Machine learning

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APA (6th Edition):

Kulesza, T. (2014). Personalizing machine learning systems with explanatory debugging. (Doctoral Dissertation). Oregon State University. Retrieved from http://hdl.handle.net/1957/54622

Chicago Manual of Style (16th Edition):

Kulesza, Todd. “Personalizing machine learning systems with explanatory debugging.” 2014. Doctoral Dissertation, Oregon State University. Accessed April 26, 2019. http://hdl.handle.net/1957/54622.

MLA Handbook (7th Edition):

Kulesza, Todd. “Personalizing machine learning systems with explanatory debugging.” 2014. Web. 26 Apr 2019.

Vancouver:

Kulesza T. Personalizing machine learning systems with explanatory debugging. [Internet] [Doctoral dissertation]. Oregon State University; 2014. [cited 2019 Apr 26]. Available from: http://hdl.handle.net/1957/54622.

Council of Science Editors:

Kulesza T. Personalizing machine learning systems with explanatory debugging. [Doctoral Dissertation]. Oregon State University; 2014. Available from: http://hdl.handle.net/1957/54622

9. Bryant, Douglas W. (Douglas Wesley). Algorithms for massive biological datasets.

Degree: PhD, Computer Science, 2011, Oregon State University

 Within the past several years the technology of high-throughput sequencing has transformed the study of biology by offering unprecedented access to life's fundamental building block,… (more)

Subjects/Keywords: High-throughput sequencing; Nucleotide sequence  – Computer programs

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APA (6th Edition):

Bryant, D. W. (. W. (2011). Algorithms for massive biological datasets. (Doctoral Dissertation). Oregon State University. Retrieved from http://hdl.handle.net/1957/25504

Chicago Manual of Style (16th Edition):

Bryant, Douglas W (Douglas Wesley). “Algorithms for massive biological datasets.” 2011. Doctoral Dissertation, Oregon State University. Accessed April 26, 2019. http://hdl.handle.net/1957/25504.

MLA Handbook (7th Edition):

Bryant, Douglas W (Douglas Wesley). “Algorithms for massive biological datasets.” 2011. Web. 26 Apr 2019.

Vancouver:

Bryant DW(W. Algorithms for massive biological datasets. [Internet] [Doctoral dissertation]. Oregon State University; 2011. [cited 2019 Apr 26]. Available from: http://hdl.handle.net/1957/25504.

Council of Science Editors:

Bryant DW(W. Algorithms for massive biological datasets. [Doctoral Dissertation]. Oregon State University; 2011. Available from: http://hdl.handle.net/1957/25504

10. Anderson, Michael M. (Michael Mason). Physical activity recognition of free-living data using change-point detection algorithms and hidden Markov models.

Degree: MS, Computer Science, 2013, Oregon State University

 Physical activity recognition using accelerometer data is a rapidly emerging field with many real-world applications. Much of the previous work in this area has assumed… (more)

Subjects/Keywords: Time-series analysis

Oregon State University [37], [41]. This dataset consisted of 91 time… 

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APA (6th Edition):

Anderson, M. M. (. M. (2013). Physical activity recognition of free-living data using change-point detection algorithms and hidden Markov models. (Masters Thesis). Oregon State University. Retrieved from http://hdl.handle.net/1957/40320

Chicago Manual of Style (16th Edition):

Anderson, Michael M (Michael Mason). “Physical activity recognition of free-living data using change-point detection algorithms and hidden Markov models.” 2013. Masters Thesis, Oregon State University. Accessed April 26, 2019. http://hdl.handle.net/1957/40320.

MLA Handbook (7th Edition):

Anderson, Michael M (Michael Mason). “Physical activity recognition of free-living data using change-point detection algorithms and hidden Markov models.” 2013. Web. 26 Apr 2019.

Vancouver:

Anderson MM(M. Physical activity recognition of free-living data using change-point detection algorithms and hidden Markov models. [Internet] [Masters thesis]. Oregon State University; 2013. [cited 2019 Apr 26]. Available from: http://hdl.handle.net/1957/40320.

Council of Science Editors:

Anderson MM(M. Physical activity recognition of free-living data using change-point detection algorithms and hidden Markov models. [Masters Thesis]. Oregon State University; 2013. Available from: http://hdl.handle.net/1957/40320

11. Yu, Jun. Machine learning for improving the quality of citizen science data.

Degree: PhD, Computer Science, 2013, Oregon State University

 Citizen Science is a paradigm in which volunteers from the general public participate in scientific studies, often by performing data collection. This paradigm is especially… (more)

Subjects/Keywords: Citizen Science; Machine learning

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APA (6th Edition):

Yu, J. (2013). Machine learning for improving the quality of citizen science data. (Doctoral Dissertation). Oregon State University. Retrieved from http://hdl.handle.net/1957/44735

Chicago Manual of Style (16th Edition):

Yu, Jun. “Machine learning for improving the quality of citizen science data.” 2013. Doctoral Dissertation, Oregon State University. Accessed April 26, 2019. http://hdl.handle.net/1957/44735.

MLA Handbook (7th Edition):

Yu, Jun. “Machine learning for improving the quality of citizen science data.” 2013. Web. 26 Apr 2019.

Vancouver:

Yu J. Machine learning for improving the quality of citizen science data. [Internet] [Doctoral dissertation]. Oregon State University; 2013. [cited 2019 Apr 26]. Available from: http://hdl.handle.net/1957/44735.

Council of Science Editors:

Yu J. Machine learning for improving the quality of citizen science data. [Doctoral Dissertation]. Oregon State University; 2013. Available from: http://hdl.handle.net/1957/44735

12. Guan, Xinze. Weakly Supervised Learning for Activity Recognition from Time Series Data.

Degree: PhD, 2017, Oregon State University

 The thesis focuses on activity recognition from sensor data, which has spurred a great deal of interest due to its impact on health care and… (more)

Subjects/Keywords: Weakly Supervised Learning

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APA (6th Edition):

Guan, X. (2017). Weakly Supervised Learning for Activity Recognition from Time Series Data. (Doctoral Dissertation). Oregon State University. Retrieved from http://hdl.handle.net/1957/61530

Chicago Manual of Style (16th Edition):

Guan, Xinze. “Weakly Supervised Learning for Activity Recognition from Time Series Data.” 2017. Doctoral Dissertation, Oregon State University. Accessed April 26, 2019. http://hdl.handle.net/1957/61530.

MLA Handbook (7th Edition):

Guan, Xinze. “Weakly Supervised Learning for Activity Recognition from Time Series Data.” 2017. Web. 26 Apr 2019.

Vancouver:

Guan X. Weakly Supervised Learning for Activity Recognition from Time Series Data. [Internet] [Doctoral dissertation]. Oregon State University; 2017. [cited 2019 Apr 26]. Available from: http://hdl.handle.net/1957/61530.

Council of Science Editors:

Guan X. Weakly Supervised Learning for Activity Recognition from Time Series Data. [Doctoral Dissertation]. Oregon State University; 2017. Available from: http://hdl.handle.net/1957/61530

13. Moore, Travis (Travis Walker). Incorporating labeled features into image classification using generalized expectation.

Degree: MS, Computer Science, 2013, Oregon State University

 Image classification is a difficult problem, often requiring large training sets to get satisfactory results. However this is a task that humans perform very well,… (more)

Subjects/Keywords: Generalized expectation; Random fields

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APA (6th Edition):

Moore, T. (. W. (2013). Incorporating labeled features into image classification using generalized expectation. (Masters Thesis). Oregon State University. Retrieved from http://hdl.handle.net/1957/44793

Chicago Manual of Style (16th Edition):

Moore, Travis (Travis Walker). “Incorporating labeled features into image classification using generalized expectation.” 2013. Masters Thesis, Oregon State University. Accessed April 26, 2019. http://hdl.handle.net/1957/44793.

MLA Handbook (7th Edition):

Moore, Travis (Travis Walker). “Incorporating labeled features into image classification using generalized expectation.” 2013. Web. 26 Apr 2019.

Vancouver:

Moore T(W. Incorporating labeled features into image classification using generalized expectation. [Internet] [Masters thesis]. Oregon State University; 2013. [cited 2019 Apr 26]. Available from: http://hdl.handle.net/1957/44793.

Council of Science Editors:

Moore T(W. Incorporating labeled features into image classification using generalized expectation. [Masters Thesis]. Oregon State University; 2013. Available from: http://hdl.handle.net/1957/44793

14. Morton, Taj H. Prediction of gene transcription start sites and initiation patterns from DNA sequence content.

Degree: MS, Computer Science, 2014, Oregon State University

 The computational identification of gene Transcription Start Sites (TSSs) can provide insights into the regulation and function of genes without performing expensive experiments, particularly in… (more)

Subjects/Keywords: Genetic transcription  – Computer programs

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APA (6th Edition):

Morton, T. H. (2014). Prediction of gene transcription start sites and initiation patterns from DNA sequence content. (Masters Thesis). Oregon State University. Retrieved from http://hdl.handle.net/1957/53761

Chicago Manual of Style (16th Edition):

Morton, Taj H. “Prediction of gene transcription start sites and initiation patterns from DNA sequence content.” 2014. Masters Thesis, Oregon State University. Accessed April 26, 2019. http://hdl.handle.net/1957/53761.

MLA Handbook (7th Edition):

Morton, Taj H. “Prediction of gene transcription start sites and initiation patterns from DNA sequence content.” 2014. Web. 26 Apr 2019.

Vancouver:

Morton TH. Prediction of gene transcription start sites and initiation patterns from DNA sequence content. [Internet] [Masters thesis]. Oregon State University; 2014. [cited 2019 Apr 26]. Available from: http://hdl.handle.net/1957/53761.

Council of Science Editors:

Morton TH. Prediction of gene transcription start sites and initiation patterns from DNA sequence content. [Masters Thesis]. Oregon State University; 2014. Available from: http://hdl.handle.net/1957/53761


Oregon State University

15. Deutschman, Stephanie. Accuracy versus cost in distributed data mining.

Degree: MS, Computer Science, 2007, Oregon State University

 A basic tradeoff to consider when designing a distributed data-mining framework is the need for a compromise between the cost of communication and computation resources… (more)

Subjects/Keywords: data mining; Data mining  – Economic aspects

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APA (6th Edition):

Deutschman, S. (2007). Accuracy versus cost in distributed data mining. (Masters Thesis). Oregon State University. Retrieved from http://hdl.handle.net/1957/6226

Chicago Manual of Style (16th Edition):

Deutschman, Stephanie. “Accuracy versus cost in distributed data mining.” 2007. Masters Thesis, Oregon State University. Accessed April 26, 2019. http://hdl.handle.net/1957/6226.

MLA Handbook (7th Edition):

Deutschman, Stephanie. “Accuracy versus cost in distributed data mining.” 2007. Web. 26 Apr 2019.

Vancouver:

Deutschman S. Accuracy versus cost in distributed data mining. [Internet] [Masters thesis]. Oregon State University; 2007. [cited 2019 Apr 26]. Available from: http://hdl.handle.net/1957/6226.

Council of Science Editors:

Deutschman S. Accuracy versus cost in distributed data mining. [Masters Thesis]. Oregon State University; 2007. Available from: http://hdl.handle.net/1957/6226

.