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Applying machine learning for predic...

Bao, Xinlong.

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Applying machine learning for prediction, recommendation, and integration.

紀錄類型:	書目-語言資料,印刷品 : Monograph/item
正題名/作者:	Applying machine learning for prediction, recommendation, and integration./
作者:	Bao, Xinlong.
面頁冊數:	122 p.
附註:	Source: Dissertation Abstracts International, Volume: 70-11, Section: B, page: 6984.
Contained By:	Dissertation Abstracts International70-11B.
標題:	Artificial Intelligence. -
電子資源:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3380852
ISBN:	9781109452730

Applying machine learning for prediction, recommendation, and integration.
Bao, Xinlong.

Applying machine learning for prediction, recommendation, and integration. - 122 p.

Source: Dissertation Abstracts International, Volume: 70-11, Section: B, page: 6984.

Thesis (Ph.D.)--Oregon State University, 2009.

This dissertation explores the idea of applying machine learning technologies to help computer users find information and better organize electronic resources, by presenting the research work conducted in the following three applications: FolderPredictor, Stacking Recommendation Engines, and Integrating Learning and Reasoning.

ISBN: 9781109452730Subjects--Topical Terms:

769149
Artificial Intelligence.

Applying machine learning for prediction, recommendation, and integration.
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500 $a Source: Dissertation Abstracts International, Volume: 70-11, Section: B, page: 6984.
500 $a Adviser: Tom Dietterich.
502 $a Thesis (Ph.D.)--Oregon State University, 2009.
520 $a This dissertation explores the idea of applying machine learning technologies to help computer users find information and better organize electronic resources, by presenting the research work conducted in the following three applications: FolderPredictor, Stacking Recommendation Engines, and Integrating Learning and Reasoning.
520 $a FolderPredictor is an intelligent desktop software tool that helps the user quickly locate files on the computer. It predicts the file folder that the user will access next by applying machine learning algorithms to the user's file access history. The predicted folders are presented in existing Windows GUIs, so that the user's cost for learning new interactions is minimized. Multiple prediction algorithms are introduced and their performance is examined in two user studies. Recommender systems are one of the most popular means of assisting internet users in finding useful online information. The second part of this dissertation presents a novel way of building hybrid recommender systems by applying the idea of Stacking from ensemble learning. Properties of the input users/items, called runtime metrics, are employed as additional meta features to improve performance. The resulting system, called STREAM, outperforms each component engine and a static linear hybrid system in a movie recommendation problem.
520 $a Many desktop assistant systems help users better organize their electronic resources by incorporating machine learning components (e.g., classifiers) to make intelligent predictions. The last part of this dissertation addresses the problem of how to improve the performance of these learning components, by integrating learning and reasoning through Markov logic. Through an inference engine called the PCE, multiple classifiers are integrated via a process called relational co-training that improves the performance of each classifier based on information propagated from other classifiers.
590 $a School code: 0172.
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710 2 $a Oregon State University. $3 625720
773 0 $t Dissertation Abstracts International $g 70-11B.
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