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133
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AUSAI
2008
Springer
15 years 4 months ago
Learning to Find Relevant Biological Articles without Negative Training Examples
Classifiers are traditionally learned using sets of positive and negative training examples. However, often a classifier is required, but for training only an incomplete set of pos...
Keith Noto, Milton H. Saier Jr., Charles Elkan
144
Voted
NIPS
2007
15 years 4 months ago
Multi-Task Learning via Conic Programming
When we have several related tasks, solving them simultaneously is shown to be more effective than solving them individually. This approach is called multi-task learning (MTL) and...
Tsuyoshi Kato, Hisashi Kashima, Masashi Sugiyama, ...
104
Voted
CORR
2010
Springer
104views Education» more  CORR 2010»
15 years 2 months ago
Empirical learning aided by weak domain knowledge in the form of feature importance
Standard hybrid learners that use domain knowledge require stronger knowledge that is hard and expensive to acquire. However, weaker domain knowledge can benefit from prior knowle...
Ridwan Al Iqbal
145
Voted
ICDM
2006
IEEE
164views Data Mining» more  ICDM 2006»
15 years 8 months ago
Unsupervised Learning of Tree Alignment Models for Information Extraction
We propose an algorithm for extracting fields from HTML search results. The output of the algorithm is a database table– a data structure that better lends itself to high-level...
Philip Zigoris, Damian Eads, Yi Zhang
KI
2007
Springer
15 years 2 months ago
Improving the Detection of Unknown Computer Worms Activity Using Active Learning
Detecting unknown worms is a challenging task. Extant solutions, such as anti-virus tools, rely mainly on prior explicit knowledge of specific worm signatures. As a result, after t...
Robert Moskovitch, Nir Nissim, Dima Stopel, Clint ...