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» Experimental perspectives on learning from imbalanced data
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ICASSP
2010
IEEE
13 years 8 months ago
Learning from high-dimensional noisy data via projections onto multi-dimensional ellipsoids
In this paper, we examine the problem of learning from noisecontaminated data in high-dimensional space. A new learning approach based on projections onto multi-dimensional ellips...
Liuling Gong, Dan Schonfeld
WILF
2005
Springer
194views Fuzzy Logic» more  WILF 2005»
14 years 1 months ago
Learning Bayesian Classifiers from Gene-Expression MicroArray Data
Computing methods that allow the efficient and accurate processing of experimentally gathered data play a crucial role in biological research. The aim of this paper is to present a...
Andrea Bosin, Nicoletta Dessì, Diego Libera...
IROS
2006
IEEE
132views Robotics» more  IROS 2006»
14 years 1 months ago
Supervised Learning of Topological Maps using Semantic Information Extracted from Range Data
Abstract— This paper presents an approach to create topological maps from geometric maps obtained with a mobile robot in an indoor-environment using range data. Our approach util...
Óscar Martínez Mozos, Wolfram Burgar...
ICDM
2003
IEEE
115views Data Mining» more  ICDM 2003»
14 years 1 months ago
On Precision and Recall of Multi-Attribute Data Extraction from Semistructured Sources
Machine learning techniques for data extraction from semistructured sources exhibit different precision and recall characteristics. However to date the formal relationship between...
Guizhen Yang, Saikat Mukherjee, I. V. Ramakrishnan
WAIM
2010
Springer
14 years 23 days ago
Semi-supervised Learning from Only Positive and Unlabeled Data Using Entropy
Abstract. The problem of classification from positive and unlabeled examples attracts much attention currently. However, when the number of unlabeled negative examples is very sma...
Xiaoling Wang, Zhen Xu, Chaofeng Sha, Martin Ester...