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ICML
1999
IEEE
16 years 8 months ago
Monte Carlo Hidden Markov Models: Learning Non-Parametric Models of Partially Observable Stochastic Processes
We present a learning algorithm for non-parametric hidden Markov models with continuous state and observation spaces. All necessary probability densities are approximated using sa...
Sebastian Thrun, John Langford, Dieter Fox
ALT
2008
Springer
16 years 4 months ago
Optimally Learning Social Networks with Activations and Suppressions
In this paper we consider the problem of learning hidden independent cascade social networks using exact value injection queries. These queries involve activating and suppressing a...
Dana Angluin, James Aspnes, Lev Reyzin
ICDM
2009
IEEE
155views Data Mining» more  ICDM 2009»
16 years 1 months ago
Stacked Gaussian Process Learning
—Triggered by a market relevant application that involves making joint predictions of pedestrian and public transit flows in urban areas, we address the question of how to utili...
Marion Neumann, Kristian Kersting, Zhao Xu, Daniel...
COLT
2007
Springer
16 years 1 months ago
Property Testing: A Learning Theory Perspective
Property testing deals with tasks where the goal is to distinguish between the case that an object (e.g., function or graph) has a prespecified property (e.g., the function is li...
Dana Ron
MICAI
2007
Springer
16 years 1 months ago
Weighted Instance-Based Learning Using Representative Intervals
Instance-based learning algorithms are widely used due to their capacity to approximate complex target functions; however, the performance of this kind of algorithms degrades signi...
Octavio Gómez, Eduardo F. Morales, Jes&uacu...