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» Detecting Topic Drift with Compound Topic Models
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ICDAR
2007
IEEE
14 years 1 months ago
Context-Sensitive Error Correction: Using Topic Models to Improve OCR
Modern optical character recognition software relies on human interaction to correct misrecognized characters. Even though the software often reliably identifies low-confidence ...
Michael L. Wick, Michael G. Ross, Erik G. Learned-...
INFORMATICALT
2008
196views more  INFORMATICALT 2008»
13 years 7 months ago
An Efficient and Sensitive Decision Tree Approach to Mining Concept-Drifting Data Streams
Abstract. Data stream mining has become a novel research topic of growing interest in knowledge discovery. Most proposed algorithms for data stream mining assume that each data blo...
Cheng-Jung Tsai, Chien-I Lee, Wei-Pang Yang
KDD
2007
ACM
148views Data Mining» more  KDD 2007»
14 years 7 months ago
Detecting research topics via the correlation between graphs and texts
In this paper we address the problem of detecting topics in large-scale linked document collections. Recently, topic detection has become a very active area of research due to its...
Yookyung Jo, Carl Lagoze, C. Lee Giles
MMM
2009
Springer
180views Multimedia» more  MMM 2009»
14 years 4 months ago
Personalized News Video Recommendation
In this paper, a novel framework is developed to support personalized news video recommendation. First, multi-modal information sources for news videos are seamlessly integrated an...
Hangzai Luo, Jianping Fan, Daniel A. Keim, Shin'ic...
HICSS
2005
IEEE
125views Biometrics» more  HICSS 2005»
14 years 29 days ago
Taking Topic Detection From Evaluation to Practice
Abstract— The Topic Detection and Tracking (TDT) research community investigates information retrieval methods for organizing a constantly arriving stream of news articles by the...
James Allan, Stephen M. Harding, David Fisher, Alv...