658 search results - page 1 / 132 » Arguing from Experience to Classifying Noisy Data |
DAWAK
2009
Springer
14 years 3 months ago
2009
Springer
A process, based on argumentation theory, is described for classifying very noisy data. More specifically a process founded on a concept called “arguing from experience” is des...
RSCTC
2010
Springer
13 years 9 months ago
2010
Springer
In this paper we studied re-sampling methods for learning classifiers from imbalanced data. We carried out a series of experiments on artificial data sets to explore the impact of ...
CVPR
2010
IEEE
14 years 3 months ago
2010
IEEE
The design of robust classifiers, which can contend with the noisy and outlier ridden datasets typical of computer vision, is studied. It is argued that such robustness requires l...
APIN
2004
13 years 11 months ago
2004
This paper describes the result of our study on neural learning to solve the classification problems in which data is unbalanced and noisy. We conducted the study on three differen...
ICTAI
2005
IEEE
14 years 4 months ago
2005
IEEE
Learning from noisy data is a challenging and reality issue for real-world data mining applications. Common practices include data cleansing, error detection and classifier ensemb...