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» Using Gaussian Processes to Optimize Expensive Functions
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ICML
2007
IEEE
14 years 8 months ago
Learning distance function by coding similarity
We consider the problem of learning a similarity function from a set of positive equivalence constraints, i.e. 'similar' point pairs. We define the similarity in informa...
Aharon Bar-Hillel, Daphna Weinshall
ECML
2006
Springer
13 years 11 months ago
An Adaptive Kernel Method for Semi-supervised Clustering
Semi-supervised clustering uses the limited background knowledge to aid unsupervised clustering algorithms. Recently, a kernel method for semi-supervised clustering has been introd...
Bojun Yan, Carlotta Domeniconi
LICS
2007
IEEE
14 years 1 months ago
Limits of Multi-Discounted Markov Decision Processes
Markov decision processes (MDPs) are controllable discrete event systems with stochastic transitions. The payoff received by the controller can be evaluated in different ways, dep...
Hugo Gimbert, Wieslaw Zielonka
ICML
2007
IEEE
14 years 8 months ago
On learning linear ranking functions for beam search
Beam search is used to maintain tractability in large search spaces at the expense of completeness and optimality. We study supervised learning of linear ranking functions for con...
Yuehua Xu, Alan Fern
TOG
2012
230views Communications» more  TOG 2012»
11 years 10 months ago
Decoupling algorithms from schedules for easy optimization of image processing pipelines
Using existing programming tools, writing high-performance image processing code requires sacrificing readability, portability, and modularity. We argue that this is a consequenc...
Jonathan Ragan-Kelley, Andrew Adams, Sylvain Paris...