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» An Instance Selection Approach to Multiple Instance Learning
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CORR
2010
Springer
124views Education» more  CORR 2010»
13 years 9 months ago
Online Learning of Noisy Data with Kernels
We study online learning when individual instances are corrupted by adversarially chosen random noise. We assume the noise distribution is unknown, and may change over time with n...
Nicolò Cesa-Bianchi, Shai Shalev-Shwartz, O...
SIGMOD
2010
ACM
151views Database» more  SIGMOD 2010»
13 years 9 months ago
Exploring schema similarity at multiple resolutions
Large, dynamic, and ad-hoc organizations must frequently initiate data integration and sharing efforts with insufficient awareness of how organizational data sources are related. ...
Ken Smith, Craig Bonaceto, Chris Wolf, Beth Yost, ...
ICCV
2007
IEEE
14 years 11 months ago
Efficient Mining of Frequent and Distinctive Feature Configurations
We present a novel approach to automatically find spatial configurations of local features occurring frequently on instances of a given object class, and rarely on the background....
Till Quack, Vittorio Ferrari, Bastian Leibe, Luc J...
COLING
2002
13 years 8 months ago
A Comparative Evaluation of Data-driven Models in Translation Selection of Machine Translation
We present a comparative evaluation of two data-driven models used in translation selection of English-Korean machine translation. Latent semantic analysis(LSA) and probabilistic ...
Yuseop Kim, Jeong Ho Chang, Byoung-Tak Zhang
AAAI
2011
12 years 9 months ago
A Scalable Tree-Based Approach for Joint Object and Pose Recognition
Recognizing possibly thousands of objects is a crucial capability for an autonomous agent to understand and interact with everyday environments. Practical object recognition comes...
Kevin Lai, Liefeng Bo, Xiaofeng Ren, Dieter Fox