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» Optimization on Support Vector Machines
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SMC
2010
IEEE
132views Control Systems» more  SMC 2010»
14 years 8 months ago
Selection of SIFT feature points for scene description in robot vision
This paper presents a method for selection of SIFT(Scale-Invariant Feature Transform) feature points using OC-SVM (One Class-Support Vector Machines). We proposed the method for au...
Yuya Utsumi, Masahiro Tsukada, Hirokazu Madokoro, ...
TIFS
2010
128views more  TIFS 2010»
14 years 8 months ago
Steganalysis by subtractive pixel adjacency matrix
This paper presents a novel method for detection of steganographic methods that embed in the spatial domain by adding a low-amplitude independent stego signal, an example of which...
Tomás Pevný, Patrick Bas, Jessica J....
TKDE
2010
168views more  TKDE 2010»
14 years 8 months ago
Completely Lazy Learning
—Local classifiers are sometimes called lazy learners because they do not train a classifier until presented with a test sample. However, such methods are generally not complet...
Eric K. Garcia, Sergey Feldman, Maya R. Gupta, San...
EMNLP
2010
14 years 8 months ago
Automatic Detection and Classification of Social Events
In this paper we introduce the new task of social event extraction from text. We distinguish two broad types of social events depending on whether only one or both parties are awa...
Apoorv Agarwal, Owen Rambow
GCB
2010
Springer
204views Biometrics» more  GCB 2010»
14 years 8 months ago
Learning Pathway-based Decision Rules to Classify Microarray Cancer Samples
: Despite recent advances in DNA chip technology current microarray gene expression studies are still affected by high noise levels, small sample sizes and large numbers of uninfor...
Enrico Glaab, Jonathan M. Garibaldi, Natalio Krasn...