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112
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PAKDD
2000
ACM
161views Data Mining» more  PAKDD 2000»
15 years 3 months ago
Adaptive Boosting for Spatial Functions with Unstable Driving Attributes
Combining multiple global models (e.g. back-propagation based neural networks) is an effective technique for improving classification accuracy by reducing a variance through manipu...
Aleksandar Lazarevic, Tim Fiez, Zoran Obradovic
ICPR
2000
IEEE
15 years 4 months ago
A New Segmentation Approach for Handwritten Digits
This article deals with a new segmentation approach applied to unconstrained handwritten digits. The novelty of the proposed algorithm is based on the combination of two types of ...
Luiz E. Soares de Oliveira, Edouard Lethelier, Fl&...
CLEF
2010
Springer
15 years 22 days ago
myClass: A Mature Tool for Patent Classification
In this task 2,000 patents in three languages (English, French and German) were to be classified among approximately 600 categories. We used a classifier based on neural networks ...
Jacques Guyot, Karim Benzineb, Gilles Falquet
AAAI
2004
15 years 1 months ago
Online Parallel Boosting
This paper presents a new boosting (arcing) algorithm called POCA, Parallel Online Continuous Arcing. Unlike traditional boosting algorithms (such as Arc-x4 and Adaboost), that co...
Jesse A. Reichler, Harlan D. Harris, Michael A. Sa...
107
Voted
IJCNN
2006
IEEE
15 years 5 months ago
A Monte Carlo Sequential Estimation for Point Process Optimum Filtering
— Adaptive filtering is normally utilized to estimate system states or outputs from continuous valued observations, and it is of limited use when the observations are discrete e...
Yiwen Wang 0002, António R. C. Paiva, Jose ...