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NIPS
2008
15 years 8 months ago
Non-stationary dynamic Bayesian networks
Abstract: Structure learning of dynamic Bayesian networks provide a principled mechanism for identifying conditional dependencies in time-series data. This learning procedure assum...
Joshua W. Robinson, Alexander J. Hartemink
PAMI
2011
15 years 1 months ago
Greedy Learning of Binary Latent Trees
—Inferring latent structures from observations helps to model and possibly also understand underlying data generating processes. A rich class of latent structures are the latent ...
Stefan Harmeling, Christopher K. I. Williams
TMA
2010
Springer
150views Management» more  TMA 2010»
15 years 4 months ago
Validation and Improvement of the Lossy Difference Aggregator to Measure Packet Delays
One-way packet delay is an important network performance metric. Recently, a new data structure called Lossy Difference Aggregator (LDA) has been proposed to estimate this metric m...
Josep Sanjuàs-Cuxart, Pere Barlet-Ros, Jose...
SIGMOD
2001
ACM
124views Database» more  SIGMOD 2001»
16 years 6 months ago
Space-Efficient Online Computation of Quantile Summaries
An -appro ximate quantile summary of a sequence of N elements is a data structure that can answer quantile queries about the sequence to within a precision of N. We presen t a new...
Michael Greenwald, Sanjeev Khanna
ICPR
2008
IEEE
16 years 1 months ago
Manifold denoising with Gaussian Process Latent Variable Models
For a finite set of points lying on a lower dimensional manifold embedded in a high-dimensional data space, algorithms have been developed to study the manifold structure. Howeve...
Yan Gao, Kap Luk Chan, Wei-Yun Yau