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» Approximate Inference and Constrained Optimization
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WSC
2007
13 years 10 months ago
New greedy myopic and existing asymptotic sequential selection procedures: preliminary empirical results
Statistical selection procedures can identify the best of a finite set of alternatives, where “best” is defined in terms of the unknown expected value of each alternative’...
Stephen E. Chick, Jürgen Branke, Christian Sc...
ICML
2010
IEEE
13 years 8 months ago
Gaussian Processes Multiple Instance Learning
This paper proposes a multiple instance learning (MIL) algorithm for Gaussian processes (GP). The GP-MIL model inherits two crucial benefits from GP: (i) a principle manner of lea...
Minyoung Kim, Fernando De la Torre
IJCAI
1997
13 years 9 months ago
Combining Probabilistic Population Codes
We study the problemof statisticallycorrect inference in networks whose basic representations are population codes. Population codes are ubiquitous in the brain, and involve the s...
Richard S. Zemel, Peter Dayan
EAAI
2010
119views more  EAAI 2010»
13 years 6 months ago
A heuristic-based framework to solve a complex aircraft sizing problem
Aircraft sizing studies consist in determining the main characteristics of an aircraft starting from a set of requirements. These studies can be summarized as global constrained o...
Céline Badufle, Christophe Blondel, Thierry...
ICCV
2003
IEEE
14 years 9 months ago
Mean Shift Based Clustering in High Dimensions: A Texture Classification Example
Feature space analysis is the main module in many computer vision tasks. The most popular technique, k-means clustering, however, has two inherent limitations: the clusters are co...
Bogdan Georgescu, Ilan Shimshoni, Peter Meer