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» Training Methods for Adaptive Boosting of Neural Networks
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IJCNN
2006
IEEE
15 years 3 months ago
Pattern Selection for Support Vector Regression based on Sparseness and Variability
— Support Vector Machine has been well received in machine learning community with its theoretical as well as practical value. However, since its training time complexity is cubi...
Jiyoung Sun, Sungzoon Cho
ECAI
2004
Springer
15 years 3 months ago
Towards Efficient Learning of Neural Network Ensembles from Arbitrarily Large Datasets
Advances in data collection technologies allow accumulation of large and high dimensional datasets and provide opportunities for learning high quality classification and regression...
Kang Peng, Zoran Obradovic, Slobodan Vucetic
IJON
2002
120views more  IJON 2002»
14 years 9 months ago
The recognition and analysis of animate objects using neural networks and active contour models
: In this paper we describe a method for tracking walking humans in the visual field. Active contour models are used to track moving objects in a sequence of images. The resulting ...
Ken Tabb, Neil Davey, Rod Adams, Stella J. George
CORR
2010
Springer
179views Education» more  CORR 2010»
14 years 6 months ago
Comparison of Support Vector Machine and Back Propagation Neural Network in Evaluating the Enterprise Financial Distress
Recently, applying the novel data mining techniques for evaluating enterprise financial distress has received much research alternation. Support Vector Machine (SVM) and back prop...
Ming-Chang Lee, To Chang
CVPR
2009
IEEE
1002views Computer Vision» more  CVPR 2009»
16 years 4 months ago
Classifier Grids for Robust Adaptive Object Detection
In this paper we present an adaptive but robust object detector for static cameras by introducing classifier grids. Instead of using a sliding window for object detection we pro...
Peter M. Roth, Sabine Sternig, Helmut Grabner, Hor...