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» Adapting SVM Classifiers to Data with Shifted Distributions
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DRR
2008
13 years 10 months ago
Whole-book recognition using mutual-entropy-driven model adaptation
We describe an approach to unsupervised high-accuracy recognition of the textual contents of an entire book using fully automatic mutual-entropy-based model adaptation. Given imag...
Pingping Xiu, Henry S. Baird
BMCBI
2006
151views more  BMCBI 2006»
13 years 9 months ago
Machine learning and word sense disambiguation in the biomedical domain: design and evaluation issues
Background: Word sense disambiguation (WSD) is critical in the biomedical domain for improving the precision of natural language processing (NLP), text mining, and information ret...
Hua Xu, Marianthi Markatou, Rositsa Dimova, Hongfa...
KDD
1998
ACM
112views Data Mining» more  KDD 1998»
14 years 1 months ago
Evaluating Usefulness for Dynamic Classification
This paper develops the concept of usefulness in the context of supervised learning. We argue that usefulness can be used to improve the performance of classification rules (as me...
Gholamreza Nakhaeizadeh, Charles Taylor, Carsten L...
FLAIRS
2007
13 years 11 months ago
Managing Dynamic Contexts Using Failure-Driven Stochastic Models
We describe an architecture for representing and managing context shifts that supports dynamic data interpretation. This architecture utilizes two layers of learning and three lay...
Nikita A. Sakhanenko, George F. Luger, Carl R. Ste...
BMCBI
2007
157views more  BMCBI 2007»
13 years 9 months ago
Impact of image segmentation on high-content screening data quality for SK-BR-3 cells
Background: High content screening (HCS) is a powerful method for the exploration of cellular signalling and morphology that is rapidly being adopted in cancer research. HCS uses ...
Andrew A. Hill, Peter LaPan, Yizheng Li, Steve Han...