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» Evaluating algorithms that learn from data streams
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KDD
2006
ACM
118views Data Mining» more  KDD 2006»
16 years 7 months ago
Mining for proposal reviewers: lessons learned at the national science foundation
In this paper, we discuss a prototype application deployed at the U.S. National Science Foundation for assisting program directors in identifying reviewers for proposals. The appl...
Seth Hettich, Michael J. Pazzani
WWW
2007
ACM
16 years 7 months ago
Hierarchical, perceptron-like learning for ontology-based information extraction
Recent work on ontology-based Information Extraction (IE) has tried to make use of knowledge from the target ontology in order to improve semantic annotation results. However, ver...
Yaoyong Li, Kalina Bontcheva
NIPS
2007
15 years 8 months ago
Sparse Feature Learning for Deep Belief Networks
Unsupervised learning algorithms aim to discover the structure hidden in the data, and to learn representations that are more suitable as input to a supervised machine than the ra...
Marc'Aurelio Ranzato, Y-Lan Boureau, Yann LeCun
207
Voted
IJMMS
2007
116views more  IJMMS 2007»
15 years 6 months ago
Modeling and evaluating empathy in embodied companion agents
Affective reasoning plays an increasingly important role in cognitive accounts of social interaction. Humans continuously assess one another's situational context, modify the...
Scott W. McQuiggan, James C. Lester
KDD
2005
ACM
158views Data Mining» more  KDD 2005»
16 years 7 months ago
Adversarial learning
Many classification tasks, such as spam filtering, intrusion detection, and terrorism detection, are complicated by an adversary who wishes to avoid detection. Previous work on ad...
Daniel Lowd, Christopher Meek