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AIED
2009
Springer
14 years 3 months ago
Intelligent Learning Object Guide (iLOG): A Framework for Automatic Empirically-Based Metadata Generation
Abstract. We present a framework for the automatic annotation of learning objects (LOs) with empirical usage metadata. Our implementation of the Intelligent Learning Object Guide (...
S. A. Riley, Lee Dee Miller, Leen-Kiat Soh, Ashok ...
GECCO
2007
Springer
187views Optimization» more  GECCO 2007»
14 years 2 months ago
Defining implicit objective functions for design problems
In many design tasks it is difficult to explicitly define an objective function. This paper uses machine learning to derive an objective in a feature space based on selected examp...
Sean Hanna
TIP
2008
125views more  TIP 2008»
13 years 8 months ago
Integrating Color and Shape-Texture Features for Adaptive Real-Time Object Tracking
We extend the standard mean-shift tracking algorithm to an adaptive tracker by selecting reliable features from color and shape-texture cues according to their descriptive ability....
Junqiu Wang, Yasushi Yagi
ICDAR
2003
IEEE
14 years 1 months ago
Unsupervised Feature Selection Using Multi-Objective Genetic Algorithms for Handwritten Word Recognition
In this paper a methodology for feature selection in unsupervised learning is proposed. It makes use of a multiobjective genetic algorithm where the minimization of the number of ...
Marisa E. Morita, Robert Sabourin, Flávio B...
ICCCN
2007
IEEE
13 years 8 months ago
Online Selection of Tracking Features using AdaBoost
In this paper, a novel feature selection algorithm for object tracking is proposed. This algorithm performs more robust than the previous works by taking the correlation between f...
Ying-Jia Yeh, Chiou-Ting Hsu