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ICML
2008
IEEE
14 years 6 months ago
Laplace maximum margin Markov networks
We propose Laplace max-margin Markov networks (LapM3 N), and a general class of Bayesian M3 N (BM3 N) of which the LapM3 N is a special case with sparse structural bias, for robus...
Jun Zhu, Eric P. Xing, Bo Zhang
SADM
2010
141views more  SADM 2010»
13 years 23 days ago
A parametric mixture model for clustering multivariate binary data
: The traditional latent class analysis (LCA) uses a mixture model with binary responses on each subject that are independent conditional on cluster membership. However, in many pr...
Ajit C. Tamhane, Dingxi Qiu, Bruce E. Ankenman
COLT
2010
Springer
13 years 4 months ago
Following the Flattened Leader
We analyze the regret, measured in terms of log loss, of the maximum likelihood (ML) sequential prediction strategy. This "follow the leader" strategy also defines one o...
Wojciech Kotlowski, Peter Grünwald, Steven de...
CORR
2010
Springer
124views Education» more  CORR 2010»
13 years 6 months ago
Online Learning of Noisy Data with Kernels
We study online learning when individual instances are corrupted by adversarially chosen random noise. We assume the noise distribution is unknown, and may change over time with n...
Nicolò Cesa-Bianchi, Shai Shalev-Shwartz, O...
ALT
1999
Springer
13 years 10 months ago
PAC Learning with Nasty Noise
We introduce a new model for learning in the presence of noise, which we call the Nasty Noise model. This model generalizes previously considered models of learning with noise. Th...
Nader H. Bshouty, Nadav Eiron, Eyal Kushilevitz