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» On Combining Classifiers
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FSS
2008
110views more  FSS 2008»
13 years 9 months ago
Learning valued preference structures for solving classification problems
This paper introduces a new approach to classification which combines pairwise decomposition techniques with ideas and tools from fuzzy preference modeling. More specifically, our...
Eyke Hüllermeier, Klaus Brinker
ICIP
2003
IEEE
14 years 10 months ago
Pixel classification through divergence-based integration of texture methods with conflict resolution
This paper presents a new technique for combining multiple texture feature extraction methods in order to classify the pixels of an input image into a set of texture models of int...
Domènec Puig, Miguel Angel García
ECCV
2006
Springer
13 years 11 months ago
Recognition and Segmentation of 3-D Human Action Using HMM and Multi-class AdaBoost
Our goal is to automatically segment and recognize basic human actions, such as stand, walk and wave hands, from a sequence of joint positions or pose angles. Such recognition is d...
Fengjun Lv, Ramakant Nevatia
CVPR
2005
IEEE
14 years 11 months ago
Online Detection and Classification of Moving Objects Using Progressively Improving Detectors
Boosting based detection methods have successfully been used for robust detection of faces and pedestrians. However, a very large amount of labeled examples are required for train...
Omar Javed, Saad Ali, Mubarak Shah
ICPR
2008
IEEE
14 years 10 months ago
Human tracking based on Soft Decision Feature and online real boosting
Online Boosting is an effective incremental learning method which can update weak classifiers efficiently according to the object being trackedt. It is a promising technique for o...
Hironobu Fujiyoshi, Masato Kawade, Shihong Lao, Ta...