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NC
2006
132views Neural Networks» more  NC 2006»
13 years 9 months ago
Learning short multivariate time series models through evolutionary and sparse matrix computation
Multivariate Time Series (MTS) data are widely available in different fields including medicine, finance, bioinformatics, science and engineering. Modelling MTS data accurately is...
Stephen Swift, Joost N. Kok, Xiaohui Liu
TSMC
2002
105views more  TSMC 2002»
13 years 9 months ago
On the use of learning automata in the control of broadcast networks: a methodology
Due to its fixed assignment nature, the well-known time division multiple access (TDMA) protocol suffers from poor performance when the offered traffic is bursty. In this paper, an...
Georgios I. Papadimitriou, Mohammad S. Obaidat, An...
NN
2007
Springer
106views Neural Networks» more  NN 2007»
13 years 8 months ago
Machine learning approach to color constancy
A number of machine learning (ML) techniques have recently been proposed to solve color constancy problem in computer vision. Neural networks (NNs) and support vector regression (...
Vivek Agarwal, Andrei V. Gribok, Mongi A. Abidi
AAAI
2011
12 years 9 months ago
Using Semantic Cues to Learn Syntax
We present a method for dependency grammar induction that utilizes sparse annotations of semantic relations. This induction set-up is attractive because such annotations provide u...
Tahira Naseem, Regina Barzilay
ICML
2007
IEEE
14 years 10 months ago
Dimensionality reduction and generalization
In this paper we investigate the regularization property of Kernel Principal Component Analysis (KPCA), by studying its application as a preprocessing step to supervised learning ...
Sofia Mosci, Lorenzo Rosasco, Alessandro Verri