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» Approximation Methods for Supervised Learning
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IJCNN
2007
IEEE
14 years 4 months ago
Parallel Learning of Large Fuzzy Cognitive Maps
— Fuzzy Cognitive Maps (FCMs) are a class of discrete-time Artificial Neural Networks that are used to model dynamic systems. A recently introduced supervised learning method, wh...
Wojciech Stach, Lukasz A. Kurgan, Witold Pedrycz
ICASSP
2011
IEEE
13 years 1 months ago
A supervised approach to movie emotion tracking
In this paper, we present experiments on continuous time, continuous scale affective movie content recognition (emotion tracking). A major obstacle for emotion research has been t...
Nikos Malandrakis, Alexandros Potamianos, Georgios...
ISBI
2009
IEEE
14 years 4 months ago
Quantitative Comparison of Spot Detection Methods in Live-Cell Fluorescence Microscopy Imaging
In live-cell fluorescence microscopy imaging, quantitative analysis of biological image data generally involves the detection of many subresolution objects, appearing as diffract...
Ihor Smal, Marco Loog, Wiro J. Niessen, Erik H. W....
NIPS
2007
13 years 11 months ago
Fast and Scalable Training of Semi-Supervised CRFs with Application to Activity Recognition
We present a new and efficient semi-supervised training method for parameter estimation and feature selection in conditional random fields (CRFs). In real-world applications suc...
Maryam Mahdaviani, Tanzeem Choudhury
KBS
2006
150views more  KBS 2006»
13 years 10 months ago
Clusterer ensemble
Ensemble methods that train multiple learners and then combine their predictions have been shown to be very effective in supervised learning. This paper explores ensemble methods ...
Zhi-Hua Zhou, Wei Tang