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» Supervised Feature Extraction Using Hilbert-Schmidt Norms
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ICMCS
2005
IEEE
145views Multimedia» more  ICMCS 2005»
15 years 5 months ago
From Physiological Signals to Emotions: Implementing and Comparing Selected Methods for Feature Extraction and Classification
Little attention has been paid so far to physiological signals for emotion recognition compared to audio-visual emotion channels, such as facial expressions or speech. In this pap...
Johannes Wagner, Jonghwa Kim, Elisabeth Andr&eacut...
IJCNLP
2005
Springer
15 years 5 months ago
Relation Extraction Using Support Vector Machine
This paper presents a supervised approach for relation extraction. We apply Support Vector Machines to detect and classify the relations in Automatic Content Extraction (ACE) corpu...
Gum-Won Hong
ICIC
2009
Springer
15 years 4 months ago
Dimension Reduction Using Semi-Supervised Locally Linear Embedding for Plant Leaf Classification
Plant has plenty use in foodstuff, medicine and industry, and is also vitally important for environmental protection. So, it is important and urgent to recognize and classify plant...
Shanwen Zhang, Kwok-Wing Chau
TKDE
2011
479views more  TKDE 2011»
14 years 6 months ago
Learning Semi-Riemannian Metrics for Semisupervised Feature Extraction
—Discriminant feature extraction plays a central role in pattern recognition and classification. Linear Discriminant Analysis (LDA) is a traditional algorithm for supervised feat...
Wei Zhang, Zhouchen Lin, Xiaoou Tang
PR
2010
186views more  PR 2010»
14 years 10 months ago
Feature extraction by learning Lorentzian metric tensor and its extensions
We develop a supervised dimensionality reduction method, called Lorentzian Discriminant Projection (LDP), for feature extraction and classification. Our method represents the str...
Risheng Liu, Zhouchen Lin, Zhixun Su, Kewei Tang