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» Bounds for Linear Multi-Task Learning
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COLT
2004
Springer
15 years 5 months ago
Regularization and Semi-supervised Learning on Large Graphs
We consider the problem of labeling a partially labeled graph. This setting may arise in a number of situations from survey sampling to information retrieval to pattern recognition...
Mikhail Belkin, Irina Matveeva, Partha Niyogi
ML
2002
ACM
167views Machine Learning» more  ML 2002»
14 years 11 months ago
Linear Programming Boosting via Column Generation
We examine linear program (LP) approaches to boosting and demonstrate their efficient solution using LPBoost, a column generation based simplex method. We formulate the problem as...
Ayhan Demiriz, Kristin P. Bennett, John Shawe-Tayl...
CORR
2011
Springer
192views Education» more  CORR 2011»
14 years 6 months ago
Distribution-Independent Evolvability of Linear Threshold Functions
Valiant’s (2007) model of evolvability models the evolutionary process of acquiring useful functionality as a restricted form of learning from random examples. Linear threshold ...
Vitaly Feldman
ICML
2010
IEEE
15 years 23 days ago
Efficient Learning with Partially Observed Attributes
We describe and analyze efficient algorithms for learning a linear predictor from examples when the learner can only view a few attributes of each training example. This is the ca...
Nicolò Cesa-Bianchi, Shai Shalev-Shwartz, O...
COLT
2001
Springer
15 years 4 months ago
Smooth Boosting and Learning with Malicious Noise
We describe a new boosting algorithm which generates only smooth distributions which do not assign too much weight to any single example. We show that this new boosting algorithm ...
Rocco A. Servedio