5463 search results - page 149 / 1093 » Evaluating algorithms that learn from data streams |
125
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ICML
2009
IEEE
15 years 11 months ago
2009
IEEE
We describe a probabilistic approach for supervised learning when we have multiple experts/annotators providing (possibly noisy) labels but no absolute gold standard. The proposed...
182
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ICDM
2009
IEEE
15 years 2 months ago
2009
IEEE
The amount of text data on the Internet is growing at a very fast rate. Online text repositories for news agencies, digital libraries and other organizations currently store gigaan...
155
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ICIP
2007
IEEE
15 years 4 months ago
2007
IEEE
The Asynchronous Hidden Markov Model (AHMM) models the joint likelihood of two observation sequences, even if the streams are not synchronised. We explain this concept and how the...
120
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ICML
2000
IEEE
16 years 5 months ago
2000
IEEE
Most machine learning algorithms are lazy: they extract from the training set the minimum information needed to predict its labels. Unfortunately, this often leads to models that ...
197
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KDD
2010
ACM
15 years 2 months ago
2010
ACM
The dynamic marketplace in online advertising calls for ranking systems that are optimized to consistently promote and capitalize better performing ads. The streaming nature of on...