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» Using Anonymized Data for Classification
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93
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LREC
2010
131views Education» more  LREC 2010»
15 years 4 months ago
A Semi-supervised Type-based Classification of Adjectives: Distinguishing Properties and Relations
We present a semi-supervised machine-learning approach for the classification of adjectives into property- vs. relationdenoting adjectives, a distinction that is highly relevant f...
Matthias Hartung, Anette Frank
143
Voted
ICIAP
2009
ACM
16 years 3 months ago
Multi-class Binary Symbol Classification with Circular Blurred Shape Models
Multi-class binary symbol classification requires the use of rich descriptors and robust classifiers. Shape representation is a difficult task because of several symbol distortions...
Sergio Escalera, Alicia Fornés, Oriol Pujol...
118
Voted
IDEAS
2007
IEEE
71views Database» more  IDEAS 2007»
15 years 9 months ago
Feature Space Enrichment by Incorporation of Implicit Features for Effective Classification
Feature Space Conversion for classifiers is the process by which the data that is to be fed into the classifier is transformed from one form to another. The motivation behind doin...
Abhishek Srivastava, Osmar R. Zaïane, Maria-L...
111
Voted
DAGM
2010
Springer
15 years 2 months ago
Learning of Optimal Illumination for Material Classification
We present a method to classify materials in illumination series data. An illumination series is acquired using a device which is capable to generate arbitrary lighting environment...
Markus Jehle, Christoph Sommer, Bernd Jähne
119
Voted
KDD
2007
ACM
190views Data Mining» more  KDD 2007»
16 years 3 months ago
Model-shared subspace boosting for multi-label classification
Typical approaches to multi-label classification problem require learning an independent classifier for every label from all the examples and features. This can become a computati...
Rong Yan, Jelena Tesic, John R. Smith