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» Evaluating learning algorithms and classifiers
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FLAIRS
2006
15 years 5 months ago
Using Validation Sets to Avoid Overfitting in AdaBoost
AdaBoost is a well known, effective technique for increasing the accuracy of learning algorithms. However, it has the potential to overfit the training set because its objective i...
Tom Bylander, Lisa Tate
IJCNN
2006
IEEE
15 years 10 months ago
Nominal-scale Evolving Connectionist Systems
— A method is presented for extending the Evolving Connectionist System (ECoS) algorithm that allows it to explicitly represent and learn nominal-scale data without the need for ...
Michael J. Watts
OTM
2010
Springer
15 years 2 months ago
Towards Evaluating GRASIM for Ontology-Based Data Matching
The GRASIM (Graph-Aided Similarity calculation) algorithm is designed to solve the problem of ontology-based data matching. We subdivide the matching problem into the ones of restr...
Yan Tang
CVPR
2009
IEEE
16 years 11 months ago
Learning to Track with Multiple Observers
We propose a novel approach to designing algorithms for object tracking based on fusing multiple observation models. As the space of possible observation models is too large for...
Björn Stenger, Roberto Cipolla, Thomas Woodle...
ICMCS
2006
IEEE
153views Multimedia» more  ICMCS 2006»
15 years 10 months ago
Learning-Based Interactive Video Retrieval System
This paper presents an interactive video event retrieval system based on improved adaboost learning. This system consists of three main steps. Firstly, a long video sequence is pa...
Chi-Jiunn Wu, Hui-Chi Zeng, Szu-Hao Huang, Shang-H...