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PKDD
2010
Springer
184views Data Mining» more  PKDD 2010»
13 years 6 months ago
Shift-Invariant Grouped Multi-task Learning for Gaussian Processes
Multi-task learning leverages shared information among data sets to improve the learning performance of individual tasks. The paper applies this framework for data where each task ...
Yuyang Wang, Roni Khardon, Pavlos Protopapas
GIS
2002
ACM
13 years 7 months ago
Aggregation and comparison of trajectories
Dealing with moving objects necessitates having available complete geographical traces for determining exact or possible locations that objects have had, have or will have. This i...
Nirvana Meratnia, Rolf A. de By
NN
1997
Springer
174views Neural Networks» more  NN 1997»
13 years 11 months ago
Learning Dynamic Bayesian Networks
Bayesian networks are directed acyclic graphs that represent dependencies between variables in a probabilistic model. Many time series models, including the hidden Markov models (H...
Zoubin Ghahramani
BMCBI
2010
142views more  BMCBI 2010»
13 years 7 months ago
pplacer: linear time maximum-likelihood and Bayesian phylogenetic placement of sequences onto a fixed reference tree
Background: Likelihood-based phylogenetic inference is generally considered to be the most reliable classification method for unknown sequences. However, traditional likelihood-ba...
Frederick A. Matsen III, Robin B. Kodner, E. Virgi...
BMCBI
2011
12 years 11 months ago
A Simple Approach to Ranking Differentially Expressed Gene Expression Time Courses through Gaussian Process Regression
Background: The analysis of gene expression from time series underpins many biological studies. Two basic forms of analysis recur for data of this type: removing inactive (quiet) ...
Alfredo A. Kalaitzis, Neil D. Lawrence