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» The Localization Hypothesis and Machines
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ICANN
2001
Springer
14 years 2 months ago
Incremental Support Vector Machine Learning: A Local Approach
Abstract. In this paper, we propose and study a new on-line algorithm for learning a SVM based on Radial Basis Function Kernel: Local Incremental Learning of SVM or LISVM. Our meth...
Liva Ralaivola, Florence d'Alché-Buc
TAL
2010
Springer
13 years 8 months ago
OpenMaTrEx: A Free/Open-Source Marker-Driven Example-Based Machine Translation System
Abstract. We describe OpenMaTrEx, a free/open-source examplebased machine translation (EBMT) system based on the marker hypothesis, comprising a marker-driven chunker, a collection...
Sandipan Dandapat, Mikel L. Forcada, Declan Groves...
ESWA
2007
127views more  ESWA 2007»
13 years 9 months ago
Clustering support vector machines for protein local structure prediction
Understanding the sequence-to-structure relationship is a central task in bioinformatics research. Adequate knowledge about this relationship can potentially improve accuracy for ...
Wei Zhong, Jieyue He, Robert W. Harrison, Phang C....
ICDM
2008
IEEE
99views Data Mining» more  ICDM 2008»
14 years 4 months ago
Kernels for the Investigation of Localized Spatiotemporal Transitions of Drought with Support Vector Machines
We present and discuss several spatiotemporal kernels designed to mine real-life and simulated data in support of drought prediction. We implement and empirically validate these k...
Matthew W. Collier, Amy McGovern
IROS
2009
IEEE
162views Robotics» more  IROS 2009»
14 years 4 months ago
Intelligent vehicle localization using GPS, compass, and machine vision
— Intelligent vehicles require accurate localization relative to a map to ensure safe travel. GPS sensors are among the most useful sensors for outdoor localization, but they sti...
Somphop Limsoonthrakul, Matthew N. Dailey, Manukid...