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» Learning of Boolean Functions Using Support Vector Machines
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CIKM
2008
Springer
13 years 11 months ago
Learning a two-stage SVM/CRF sequence classifier
Learning a sequence classifier means learning to predict a sequence of output tags based on a set of input data items. For example, recognizing that a handwritten word is "ca...
Guilherme Hoefel, Charles Elkan
ICRA
2008
IEEE
170views Robotics» more  ICRA 2008»
14 years 3 months ago
Modeling and recognition of actions through motor primitives
— We investigate modeling and recognition of object manipulation actions for the purpose of imitation based learning in robotics. To model the process, we are using a combination...
David Martínez Mercado, Danica Kragic
ESANN
2006
13 years 10 months ago
Spatial filters for the classification of event-related potentials
Spatial filtering is a widely used dimension reduction method in electroencephalogram based brain-computer interface systems. In this paper a new algorithm is proposed, which learn...
Ulrich Hoffmann, Jean-Marc Vesin, Touradj Ebrahimi
ICDM
2003
IEEE
105views Data Mining» more  ICDM 2003»
14 years 2 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...
KDD
2000
ACM
153views Data Mining» more  KDD 2000»
14 years 23 days ago
The generalized Bayesian committee machine
In this paper we introduce the Generalized Bayesian Committee Machine (GBCM) for applications with large data sets. In particular, the GBCM can be used in the context of kernel ba...
Volker Tresp