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» Learning Models for Object Recognition
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ICML
2006
IEEE
16 years 3 months ago
Nightmare at test time: robust learning by feature deletion
When constructing a classifier from labeled data, it is important not to assign too much weight to any single input feature, in order to increase the robustness of the classifier....
Amir Globerson, Sam T. Roweis
CIKM
2009
Springer
15 years 9 months ago
Combining labeled and unlabeled data with word-class distribution learning
We describe a novel simple and highly scalable semi-supervised method called Word-Class Distribution Learning (WCDL), and apply it the task of information extraction (IE) by utili...
Yanjun Qi, Ronan Collobert, Pavel Kuksa, Koray Kav...
BMCBI
2008
228views more  BMCBI 2008»
15 years 3 months ago
Adaptive diffusion kernel learning from biological networks for protein function prediction
Background: Machine-learning tools have gained considerable attention during the last few years for analyzing biological networks for protein function prediction. Kernel methods a...
Liang Sun, Shuiwang Ji, Jieping Ye
CSL
2010
Springer
15 years 3 months ago
Improving supervised learning for meeting summarization using sampling and regression
Meeting summarization provides a concise and informative summary for the lengthy meetings and is an effective tool for efficient information access. In this paper, we focus on ext...
Shasha Xie, Yang Liu
GEOINFO
2007
15 years 4 months ago
Comparison of Machine Learning Algorithms for Mapping the Phytophysiognomies of the Brazilian Cerrado
This present work describes the classification of the Phytophysiognomies present in the Brazilian Cerrado biome through the means Artificial Intelligence; data from remote sensing ...
Luciano T. de Oliveira, Thomaz C. de A. Oliveira, ...