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» Evaluating learning algorithms and classifiers
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CVPR
2004
IEEE
16 years 1 months ago
Detecting and Reading Text in Natural Scenes
This paper gives an algorithm for detecting and reading text in natural images. The algorithm is intended for use by blind and visually impaired subjects walking through city scen...
Xiangrong Chen, Alan L. Yuille
KDD
2006
ACM
129views Data Mining» more  KDD 2006»
16 years 1 days ago
Suppressing model overfitting in mining concept-drifting data streams
Mining data streams of changing class distributions is important for real-time business decision support. The stream classifier must evolve to reflect the current class distributi...
Haixun Wang, Jian Yin, Jian Pei, Philip S. Yu, Jef...
BMCBI
2008
136views more  BMCBI 2008»
14 years 11 months ago
A comparison of machine learning algorithms for chemical toxicity classification using a simulated multi-scale data model
Background: Bioactivity profiling using high-throughput in vitro assays can reduce the cost and time required for toxicological screening of environmental chemicals and can also r...
Richard Judson, Fathi Elloumi, R. Woodrow Setzer, ...
ECML
2006
Springer
15 years 3 months ago
EM Algorithm for Symmetric Causal Independence Models
Causal independence modelling is a well-known method both for reducing the size of probability tables and for explaining the underlying mechanisms in Bayesian networks. In this pap...
Rasa Jurgelenaite, Tom Heskes
KDD
2003
ACM
157views Data Mining» more  KDD 2003»
16 years 1 days ago
Cross-training: learning probabilistic mappings between topics
Classification is a well-established operation in text mining. Given a set of labels A and a set DA of training documents tagged with these labels, a classifier learns to assign l...
Sunita Sarawagi, Soumen Chakrabarti, Shantanu Godb...