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GECCO
2008
Springer
137views Optimization» more  GECCO 2008»
13 years 10 months ago
Informative sampling for large unbalanced data sets
Selective sampling is a form of active learning which can reduce the cost of training by only drawing informative data points into the training set. This selected training set is ...
Zhenyu Lu, Anand I. Rughani, Bruce I. Tranmer, Jos...
AAI
2006
215views more  AAI 2006»
13 years 8 months ago
Extensive Evaluation of Efficient NLP-Driven Text Classification
Extensive experimental evidence is required to study the impact of text categorization approaches on real data and to assess the performance within operational scenarios. In this ...
Roberto Basili, Alessandro Moschitti, Maria Teresa...
ACL
2009
13 years 6 months ago
Learning Context-Dependent Mappings from Sentences to Logical Form
We consider the problem of learning context-dependent mappings from sentences to logical form. The training examples are sequences of sentences annotated with lambda-calculus mean...
Luke S. Zettlemoyer, Michael Collins
STOC
1993
ACM
141views Algorithms» more  STOC 1993»
14 years 26 days ago
Bounds for the computational power and learning complexity of analog neural nets
Abstract. It is shown that high-order feedforward neural nets of constant depth with piecewisepolynomial activation functions and arbitrary real weights can be simulated for Boolea...
Wolfgang Maass
COLT
2000
Springer
14 years 12 days ago
The Role of Critical Sets in Vapnik-Chervonenkis Theory
In the present paper, we present the theoretical basis, as well as an empirical validation, of a protocol designed to obtain effective VC dimension estimations in the case of a si...
Nicolas Vayatis