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» Choosing Multiple Parameters for Support Vector Machines
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TKDE
2010
168views more  TKDE 2010»
13 years 6 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...
ICASSP
2009
IEEE
14 years 2 months ago
Joint map adaptation of feature transformation and Gaussian Mixture Model for speaker recognition
This paper extends our previous work on feature transformationbased support vector machines for speaker recognition by proposing a joint MAP adaptation of feature transformation (...
Donglai Zhu, Bin Ma, Haizhou Li
ICDM
2003
IEEE
105views Data Mining» more  ICDM 2003»
14 years 1 months ago
SVM Based Models for Predicting Foreign Currency Exchange Rates
Support vector machine (SVM) has appeared as a powerful tool for forecasting forex market and demonstrated better performance over other methods, e.g., neural network or ARIMA bas...
Joarder Kamruzzaman, Ruhul A. Sarker, Iftekhar Ahm...
MVA
2007
146views Computer Vision» more  MVA 2007»
13 years 9 months ago
A SVM Based Method to Detect Color Shift Defects in IC Packages
Automated Visual Inspection (AVI) is an essential part in the manufacturing process of Integrated Circuit (IC) packages. Contamination a common defect type found in IC packages ap...
R. M. C. B. Ratnayake, Craig Hicks, M. A. Akbari
ERCIMDL
2003
Springer
165views Education» more  ERCIMDL 2003»
14 years 1 months ago
Automatic Multi-label Subject Indexing in a Multilingual Environment
Abstract. This paper presents an approach to automatically subject index fulltext documents with multiple labels based on binary support vector machines (SVM). The aim was to test ...
Boris Lauser, Andreas Hotho