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KDD
2005
ACM
168views Data Mining» more  KDD 2005»
15 years 11 months ago
Nomograms for visualizing support vector machines
We propose a simple yet potentially very effective way of visualizing trained support vector machines. Nomograms are an established model visualization technique that can graphica...
Aleks Jakulin, Martin Mozina, Janez Demsar, Ivan B...
ICML
2003
IEEE
15 years 11 months ago
Hidden Markov Support Vector Machines
This paper presents a novel discriminative learning technique for label sequences based on a combination of the two most successful learning algorithms, Support Vector Machines an...
Yasemin Altun, Ioannis Tsochantaridis, Thomas Hofm...
FSS
2007
102views more  FSS 2007»
14 years 10 months ago
Extraction of fuzzy rules from support vector machines
The relationship between support vector machines (SVMs) and Takagi–Sugeno–Kang (TSK) fuzzy systems is shown. An exact representation of SVMs as TSK fuzzy systems is given for ...
Juan Luis Castro, L. D. Flores-Hidalgo, Carlos Jav...
NIPS
2004
14 years 11 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
73
Voted
ICML
2008
IEEE
15 years 11 months ago
Stopping conditions for exact computation of leave-one-out error in support vector machines
We propose a new stopping condition for a Support Vector Machine (SVM) solver which precisely reflects the objective of the Leave-OneOut error computation. The stopping condition ...
Klaus-Robert Müller, Pavel Laskov, Vojtech Fr...