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TNN
1998
111views more  TNN 1998»
15 years 28 days ago
Asymptotic distributions associated to Oja's learning equation for neural networks
— In this paper, we perform a complete asymptotic performance analysis of the stochastic approximation algorithm (denoted subspace network learning algorithm) derived from Oja’...
Jean Pierre Delmas, Jean-Francois Cardos
BPM
2005
Springer
102views Business» more  BPM 2005»
15 years 6 months ago
Integrating Process Learning and Process Evolution - A Semantics Based Approach
Companies are developing a growing interest in aligning their information systems in a process-oriented way. However, current processaware information systems (PAIS) fail to meet p...
Stefanie Rinderle, Barbara Weber, Manfred Reichert...
NIPS
2007
15 years 2 months ago
Distributed Inference for Latent Dirichlet Allocation
We investigate the problem of learning a widely-used latent-variable model – the Latent Dirichlet Allocation (LDA) or “topic” model – using distributed computation, where ...
David Newman, Arthur Asuncion, Padhraic Smyth, Max...
ICML
2007
IEEE
16 years 2 months ago
Discriminative learning for differing training and test distributions
We address classification problems for which the training instances are governed by a distribution that is allowed to differ arbitrarily from the test distribution--problems also ...
Michael Brückner, Steffen Bickel, Tobias Sche...
ICML
2007
IEEE
16 years 2 months ago
Asymptotic Bayesian generalization error when training and test distributions are different
In supervised learning, we commonly assume that training and test data are sampled from the same distribution. However, this assumption can be violated in practice and then standa...
Keisuke Yamazaki, Klaus-Robert Müller, Masash...