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We consider the problem of classification when inputs correspond to sets of vectors.
Speaker identification and verification using gaussian mixture models
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Machine learning in automated text categorization
F. Sebastiani · 2002
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Covariance kernels from bayesian generative models
M. Seeger · 2002
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Neural networks for multi-instance learning
Z.-H. Zhou and M.L. Zhang · 2002
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A kernel between sets of vector
R. Kondor and T. Jebara · 2003
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Recognition with local features: the kernel recipe
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A kernel between unordered sets of data: the gaussian mixture approach
A fast learning algorithm for deep belief nets
G.E. Hinton, S. Osindero, and Y. Teh · 2006
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Feature space mahalanobis sequence kernels: Application to svm speaker verification
J. Louradour, K. Daoudi, and F. Bach · 2007
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Classification using discriminative restricted boltzmann machines
H. Larochelle and Y. Bengio · 2008
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Training restricted Boltzmann machines using approximations to the likelihood gradient
T. Tieleman · 2008
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Convolutional deep belief networks for scalable unsupervised learning of hierarchical representations
H. Lee, R. Grosse, R. Ranganath, and A. Y. Ng · 2009
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Probabilistic multi-instance learning by treating instances as non-i.i.d. samples
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S. Lyu · 2005
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Exponential family harmoniums with an application to information retrieval
M. Welling, M. Rosen-Zvi, and G. E. Hinton · 2005
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Z.-H. Zhou, Y.-Y. Sun, and Y.-F. Li · 2009
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A conditional random field for multi-instance learning
T. Deselaers and V. Ferrari · 2010
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Reducing label complexity by learning from bags
S. Sabato, N. Srebreo, and N. Tishby · 2010
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