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» Marginal Regression For Multitask Learning
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ICONIP
2009
13 years 6 months ago
Learning Gaussian Process Models from Uncertain Data
It is generally assumed in the traditional formulation of supervised learning that only the outputs data are uncertain. However, this assumption might be too strong for some learni...
Patrick Dallaire, Camille Besse, Brahim Chaib-draa
COLT
2004
Springer
14 years 1 months ago
An Inequality for Nearly Log-Concave Distributions with Applications to Learning
Abstract— We prove that given a nearly log-concave distribution, in any partition of the space to two well separated sets, the measure of the points that do not belong to these s...
Constantine Caramanis, Shie Mannor
NIPS
2003
13 years 9 months ago
Kernel Dimensionality Reduction for Supervised Learning
We propose a novel method of dimensionality reduction for supervised learning. Given a regression or classification problem in which we wish to predict a variable Y from an expla...
Kenji Fukumizu, Francis R. Bach, Michael I. Jordan
EDM
2009
110views Data Mining» more  EDM 2009»
13 years 6 months ago
Using Learning Decomposition and Bootstrapping with Randomization to Compare the Impact of Different Educational Interventions o
A basic question of instructional interventions is how effective it is in promoting student learning. This paper presents a study to determine the relative efficacy of different in...
Mingyu Feng, Joseph Beck, Neil T. Heffernan
IJCAI
2007
13 years 9 months ago
Simple Training of Dependency Parsers via Structured Boosting
Recently, significant progress has been made on learning structured predictors via coordinated training algorithms such as conditional random fields and maximum margin Markov ne...
Qin Iris Wang, Dekang Lin, Dale Schuurmans