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ICML
2006
IEEE
15 years 16 days ago
Collaborative ordinal regression
Ordinal regression has become an effective way of learning user preferences, but most of research only focuses on single regression problem. In this paper we introduce collaborati...
Shipeng Yu, Kai Yu, Volker Tresp, Hans-Peter Krieg...
ICML
2006
IEEE
15 years 16 days ago
Active learning via transductive experimental design
This paper considers the problem of selecting the most informative experiments x to get measurements y for learning a regression model y = f(x). We propose a novel and simple conc...
Kai Yu, Jinbo Bi, Volker Tresp
ICML
2006
IEEE
15 years 16 days ago
Semi-supervised nonlinear dimensionality reduction
The problem of nonlinear dimensionality reduction is considered. We focus on problems where prior information is available, namely, semi-supervised dimensionality reduction. It is...
Xin Yang, Haoying Fu, Hongyuan Zha, Jesse L. Barlo...
ICML
2006
IEEE
15 years 16 days ago
Fast time series classification using numerosity reduction
Many algorithms have been proposed for the problem of time series classification. However, it is clear that one-nearest-neighbor with Dynamic Time Warping (DTW) distance is except...
Xiaopeng Xi, Eamonn J. Keogh, Christian R. Shelton...
ICML
2006
IEEE
15 years 16 days ago
Predictive state representations with options
Britton Wolfe, Satinder P. Singh
ICML
2006
IEEE
15 years 16 days ago
Kernel Predictive Linear Gaussian models for nonlinear stochastic dynamical systems
The recent Predictive Linear Gaussian model (or PLG) improves upon traditional linear dynamical system models by using a predictive representation of state, which makes consistent...
David Wingate, Satinder P. Singh
ICML
2006
IEEE
15 years 16 days ago
Inference with the Universum
In this paper we study a new framework introduced by Vapnik (1998) and Vapnik (2006) that is an alternative capacity concept to the large margin approach. In the particular case o...
Fabian H. Sinz, Jason Weston, Léon Bottou, ...
ICML
2006
IEEE
15 years 16 days ago
Totally corrective boosting algorithms that maximize the margin
We consider boosting algorithms that maintain a distribution over a set of examples. At each iteration a weak hypothesis is received and the distribution is updated. We motivate t...
Gunnar Rätsch, Jun Liao, Manfred K. Warmuth