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» Learning on the Test Data: Leveraging Unseen Features
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UIST
2006
ACM
14 years 3 months ago
Enabling web browsers to augment web sites' filtering and sorting functionalities
Existing augmentations of web pages are mostly small cosmetic changes (e.g., removing ads) and minor addition of third-party content (e.g., product prices from competing sites). N...
David F. Huynh, Robert C. Miller, David R. Karger
IROS
2008
IEEE
138views Robotics» more  IROS 2008»
14 years 3 months ago
Deep belief net learning in a long-range vision system for autonomous off-road driving
Abstract— We present a learning-based approach for longrange vision that is able to accurately classify complex terrain at distances up to the horizon, thus allowing high-level s...
Raia Hadsell, Ayse Erkan, Pierre Sermanet, Marco S...
WKDD
2010
CPS
204views Data Mining» more  WKDD 2010»
14 years 2 months ago
A Scalable, Accurate Hybrid Recommender System
—Recommender systems apply machine learning techniques for filtering unseen information and can predict whether a user would like a given resource. There are three main types of...
Mustansar Ali Ghazanfar, Adam Prügel-Bennett
ANNPR
2006
Springer
14 years 27 days ago
Visual Classification of Images by Learning Geometric Appearances Through Boosting
We present a multiclass classification system for gray value images through boosting. The feature selection is done using the LPBoost algorithm which selects suitable features of a...
Martin Antenreiter, Christian Savu-Krohn, Peter Au...
ICCV
2007
IEEE
14 years 11 months ago
An Empirical Study of Object Category Recognition: Sequential Testing with Generalized Samples
In this paper we present an empirical study of object category recognition using generalized samples and a set of sequential tests. We study 33 categories, each consisting of a sm...
Liang Lin, Shaowu Peng, Jake Porway, Song Chun Zhu...