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KDD
2005
ACM
149views Data Mining» more  KDD 2005»
14 years 2 months ago
A distributed learning framework for heterogeneous data sources
We present a probabilistic model-based framework for distributed learning that takes into account privacy restrictions and is applicable to scenarios where the different sites ha...
Srujana Merugu, Joydeep Ghosh
ACL
1998
13 years 10 months ago
Learning Intonation Rules for Concept to Speech Generation
In this paper, we report on an effort to provide a general-purpose spoken language generation tool for Concept-to-Speech (CTS) applications by extending a widely used text generat...
Shimei Pan, Kathleen McKeown
BMCBI
2007
215views more  BMCBI 2007»
13 years 9 months ago
Learning causal networks from systems biology time course data: an effective model selection procedure for the vector autoregres
Background: Causal networks based on the vector autoregressive (VAR) process are a promising statistical tool for modeling regulatory interactions in a cell. However, learning the...
Rainer Opgen-Rhein, Korbinian Strimmer
CORR
2012
Springer
170views Education» more  CORR 2012»
12 years 5 months ago
What Cannot be Learned with Bethe Approximations
We address the problem of learning the parameters in graphical models when inference is intractable. A common strategy in this case is to replace the partition function with its B...
Uri Heinemann, Amir Globerson
ACCV
2010
Springer
13 years 4 months ago
Randomised Manifold Forests for Principal Angle-Based Face Recognition
Abstract. In set-based face recognition, each set of face images is often represented as a linear/nonlinear manifold and the Principal Angles (PA) or Kernel PAs are exploited to me...
Ujwal D. Bonde, Tae-Kyun Kim, K. R. Ramakrishnan