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» Incremental Construction of Structured Hidden Markov Models
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JMLR
2010
202views more  JMLR 2010»
13 years 5 months ago
Learning the Structure of Deep Sparse Graphical Models
Deep belief networks are a powerful way to model complex probability distributions. However, it is difficult to learn the structure of a belief network, particularly one with hidd...
Ryan Prescott Adams, Hanna M. Wallach, Zoubin Ghah...
ICASSP
2011
IEEE
13 years 2 months ago
Learning and inference algorithms for partially observed structured switching vector autoregressive models
We present learning and inference algorithms for a versatile class of partially observed vector autoregressive (VAR) models for multivariate time-series data. VAR models can captu...
Balakrishnan Varadarajan, Sanjeev Khudanpur
ICML
2007
IEEE
14 years 11 months ago
Multi-task learning for sequential data via iHMMs and the nested Dirichlet process
A new hierarchical nonparametric Bayesian model is proposed for the problem of multitask learning (MTL) with sequential data. Sequential data are typically modeled with a hidden M...
Kai Ni, Lawrence Carin, David B. Dunson
ECML
2004
Springer
14 years 4 months ago
Fisher Kernels for Logical Sequences
One approach to improve the accuracy of classifications based on generative models is to combine them with successful discriminative algorithms. Fisher kernels were developed to c...
Kristian Kersting, Thomas Gärtner
ATAL
2006
Springer
14 years 2 months ago
Learning executable agent behaviors from observation
We present a method for learning a human understandable, executable model of an agent's behavior using observations of its interaction with the environment. By executable we ...
Andrew Guillory, Hai Nguyen, Tucker R. Balch, Char...