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AAAI
1997
13 years 11 months ago
Effective Bayesian Inference for Stochastic Programs
In this paper, we propose a stochastic version of a general purpose functional programming language as a method of modeling stochastic processes. The language contains random choi...
Daphne Koller, David A. McAllester, Avi Pfeffer
ICML
2010
IEEE
13 years 11 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
JBI
2008
159views Bioinformatics» more  JBI 2008»
13 years 10 months ago
SEGS: Search for enriched gene sets in microarray data
Gene Ontology (GO) terms are often used to interpret the results of microarray experiments. The most common approach is to perform Fisher's exact tests to find gene sets anno...
Igor Trajkovski, Nada Lavrac, Jakub Tolar
IJON
2007
93views more  IJON 2007»
13 years 10 months ago
How much can we trust neural simulation strategies?
Despite a steady improvement of computational hardware, results of numerical simulation are still tightly bound to the simulation tool and strategy used, and may substantially var...
Michelle Rudolph, Alain Destexhe
PAMI
2007
253views more  PAMI 2007»
13 years 9 months ago
Gaussian Mean-Shift Is an EM Algorithm
The mean-shift algorithm, based on ideas proposed by Fukunaga and Hostetler (1975), is a hill-climbing algorithm on the density defined by a finite mixture or a kernel density e...
Miguel Á. Carreira-Perpiñán