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» Regularization Methods for Additive Models
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TIP
2010
182views more  TIP 2010»
15 years 1 months ago
Flexible Manifold Embedding: A Framework for Semi-Supervised and Unsupervised Dimension Reduction
We propose a unified manifold learning framework for semi-supervised and unsupervised dimension reduction by employing a simple but effective linear regression function to map the ...
Feiping Nie, Dong Xu, Ivor Wai-Hung Tsang, Changsh...
SIVP
2010
184views more  SIVP 2010»
15 years 5 months ago
Multichannel AR parameter estimation from noisy observations as an errors-in-variables issue
In various applications from radar processing to mobile communication systems based on CDMA for instance, M-AR multichannel processes are often considered and may be combined with...
Julien Petitjean, Eric Grivel, William Bobillet, P...
ICML
2007
IEEE
16 years 7 months ago
Comparisons of sequence labeling algorithms and extensions
In this paper, we survey the current state-ofart models for structured learning problems, including Hidden Markov Model (HMM), Conditional Random Fields (CRF), Averaged Perceptron...
Nam Nguyen, Yunsong Guo
LCN
2008
IEEE
16 years 1 months ago
An analysis on error servers for stochastic network calculus
—Network calculus is a recently developed theory dealing with queuing systems found in computer networks with focus on service guarantee analysis. In the current network calculus...
Jing Xie, Yuming Jiang
SSIAI
2000
IEEE
15 years 11 months ago
Pairwise Markov Random Fields and its Application in Textured Images Segmentation
The use of random fields, which allows one to take into account the spatial interaction among random variables in complex systems, is a frequent tool in numerous problems of stati...
Wojciech Pieczynski, Abdel-Nasser Tebbache