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COLING
2010
13 years 4 months ago
Active Deep Networks for Semi-Supervised Sentiment Classification
This paper presents a novel semisupervised learning algorithm called Active Deep Networks (ADN), to address the semi-supervised sentiment classification problem with active learni...
Shusen Zhou, Qingcai Chen, Xiaolong Wang
MFCS
2004
Springer
14 years 3 months ago
Approximating Boolean Functions by OBDDs
In learning theory and genetic programming, OBDDs are used to represent approximations of Boolean functions. This motivates the investigation of the OBDD complexity of approximatin...
Andre Gronemeier
ESANN
2006
13 years 11 months ago
Variants of Unsupervised Kernel Regression: General cost functions
We present an extension to a recent method for learning of nonlinear manifolds, which allows to incorporate general cost functions. We focus on the -insensitive loss and visually d...
Stefan Klanke, Helge Ritter
ACG
2003
Springer
14 years 2 months ago
Evaluation Function Tuning via Ordinal Correlation
Heuristic search effectiveness depends directly upon the quality of heuristic evaluations of states in the search space. We show why ordinal correlation is relevant to heuristic se...
Dave Gomboc, T. Anthony Marsland, Michael Buro
ICML
2009
IEEE
14 years 10 months ago
Importance weighted active learning
We propose an importance weighting framework for actively labeling samples. This technique yields practical yet sound active learning algorithms for general loss functions. Experi...
Alina Beygelzimer, Sanjoy Dasgupta, John Langford