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» Large Margin Classification Using the Perceptron Algorithm
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ACL
2009
13 years 5 months ago
Learning with Annotation Noise
It is usually assumed that the kind of noise existing in annotated data is random classification noise. Yet there is evidence that differences between annotators are not always ra...
Eyal Beigman, Beata Beigman Klebanov
APBC
2003
128views Bioinformatics» more  APBC 2003»
13 years 9 months ago
Machine Learning in DNA Microarray Analysis for Cancer Classification
The development of microarray technology has supplied a large volume of data to many fields. In particular, it has been applied to prediction and diagnosis of cancer, so that it e...
Sung-Bae Cho, Hong-Hee Won
JUCS
2008
136views more  JUCS 2008»
13 years 7 months ago
Crime Scene Representation (2D, 3D, Stereoscopic Projection) and Classification
: In this paper we provide a study about crime scenes and its features used in criminal investigations. We argue that the crime scene provides a large set of features that can be u...
Ricardo O. Abu Hana, Cinthia Obladen de Almendra F...
NIPS
2004
13 years 9 months ago
PAC-Bayes Learning of Conjunctions and Classification of Gene-Expression Data
We propose a "soft greedy" learning algorithm for building small conjunctions of simple threshold functions, called rays, defined on single real-valued attributes. We al...
Mario Marchand, Mohak Shah
CVPR
2006
IEEE
14 years 1 months ago
Learning Non-Metric Partial Similarity Based on Maximal Margin Criterion
The performance of many computer vision and machine learning algorithms critically depends on the quality of the similarity measure defined over the feature space. Previous works...
Xiaoyang Tan, Songcan Chen, Jun Li, Zhi-Hua Zhou