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AAAI
2011
8 years 2 months ago
Trajectory Regression on Road Networks
This paper addresses the task of trajectory cost prediction, a new learning task for trajectories. The goal of this task is to predict the cost for an arbitrary (possibly unknown)...
Tsuyoshi Idé, Masashi Sugiyama
CDC
2010
IEEE
166views Control Systems» more  CDC 2010»
8 years 6 months ago
Nuclear norm regularization for overparametrized Hammerstein systems
—In this paper we study the overparametrization scheme for Hammerstein systems [1] in the presence of regularization. The quality of the convex approximation is analysed, that is...
Tillmann Falck, Johan A. K. Suykens, Johan Schouke...
ISBI
2011
IEEE
8 years 6 months ago
Boosting power to detect genetic associations in imaging using multi-locus, genome-wide scans and ridge regression
Most algorithms used for imaging genetics examine statistical effects of each individual genetic variant, one at a time. We developed a new approach, based on ridge regression, to...
Omid Kohannim, Derrek P. Hibar, Jason L. Stein, Ne...
COLING
2010
8 years 9 months ago
The Bag-of-Opinions Method for Review Rating Prediction from Sparse Text Patterns
The problem addressed in this paper is to predict a user's numeric rating in a product review from the text of the review. Unigram and n-gram representations of text are comm...
Lizhen Qu, Georgiana Ifrim, Gerhard Weikum
ALT
2010
Springer
9 years 2 days ago
An Identity for Kernel Ridge Regression
This paper provides a probabilistic derivation of an identity connecting the square loss of ridge regression in on-line mode with the loss of a retrospectively best regressor. Some...
Fedor Zhdanov, Yuri Kalnishkan
NPL
2002
168views more  NPL 2002»
9 years 2 months ago
Reduced Rank Kernel Ridge Regression
Ridge regression is a classical statistical technique that attempts to address the bias-variance trade-off in the design of linear regression models. A reformulation of ridge regr...
Gavin C. Cawley, Nicola L. C. Talbot
CSDA
2006
145views more  CSDA 2006»
9 years 2 months ago
Improved predictions penalizing both slope and curvature in additive models
A new method is proposed to estimate the nonlinear functions in an additive regression model. Usually, these functions are estimated by penalized least squares, penalizing the cur...
Magne Aldrin
COLT
2007
Springer
9 years 8 months ago
Multi-view Regression Via Canonical Correlation Analysis
In the multi-view regression problem, we have a regression problem where the input variable (which is a real vector) can be partitioned into two different views, where it is assum...
Sham M. Kakade, Dean P. Foster
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