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ECAI
2004
Springer
14 years 23 days ago
A Generalized Quadratic Loss for Support Vector Machines
The standard SVM formulation for binary classification is based on the Hinge loss function, where errors are considered not correlated. Due to this, local information in the featu...
Filippo Portera, Alessandro Sperduti
NIPS
2004
13 years 8 months ago
A Topographic Support Vector Machine: Classification Using Local Label Configurations
The standard approach to the classification of objects is to consider the examples as independent and identically distributed (iid). In many real world settings, however, this ass...
Johannes Mohr, Klaus Obermayer
CSB
2005
IEEE
133views Bioinformatics» more  CSB 2005»
14 years 1 months ago
Investigation into Biomedical Literature Classification Using Support Vector Machines
Specific topic search in the PubMed Database, one of the most important information resources for scientific community, presents a big challenge to the users. The researcher typic...
Nalini Polavarapu, Shamkant B. Navathe, Ramprasad ...
TSD
2010
Springer
13 years 5 months ago
Correlation Features and a Linear Transform Specific Reproducing Kernel
Abstract. In this paper we introduce two ideas for phoneme classification: First, we derive the necessary steps to integrate linear transform into the computation of reproducing ke...
Andreas Beschorner, Dietrich Klakow
ICIP
2009
IEEE
13 years 5 months ago
Efficient reduction of support vectors in kernel-based methods
Kernel-based methods, e.g., support vector machine (SVM), produce high classification performances. However, the computation becomes time-consuming as the number of the vectors su...
Takumi Kobayashi, Nobuyuki Otsu