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» Feature Generation Using General Constructor Functions
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NIPS
1998
13 years 9 months ago
Gradient Descent for General Reinforcement Learning
A simple learning rule is derived, the VAPS algorithm, which can be instantiated to generate a wide range of new reinforcementlearning algorithms. These algorithms solve a number ...
Leemon C. Baird III, Andrew W. Moore
UAI
2008
13 years 9 months ago
Topic Models Conditioned on Arbitrary Features with Dirichlet-multinomial Regression
Although fully generative models have been successfully used to model the contents of text documents, they are often awkward to apply to combinations of text data and document met...
David M. Mimno, Andrew McCallum
PLDI
2011
ACM
12 years 10 months ago
EnerJ: approximate data types for safe and general low-power computation
Energy is increasingly a first-order concern in computer systems. Exploiting energy-accuracy trade-offs is an attractive choice in applications that can tolerate inaccuracies. Re...
Adrian Sampson, Werner Dietl, Emily Fortuna, Danus...
BMVC
2010
13 years 5 months ago
Patch-Cuts: A Graph-Based Image Segmentation Method Using Patch Features and Spatial Relations
In this paper, we present a graph-based image segmentation method (patch-cuts) that incorporates features and spatial relations obtained from image patches. In the first step, pat...
Gerd Brunner, Deepak Roy Chittajallu, Uday Kurkure...
GECCO
2005
Springer
175views Optimization» more  GECCO 2005»
14 years 1 months ago
Nonlinear feature extraction using a neuro genetic hybrid
Feature extraction is a process that extracts salient features from observed variables. It is considered a promising alternative to overcome the problems of weight and structure o...
Yung-Keun Kwon, Byung Ro Moon