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JMLR
2010
161views more  JMLR 2010»
14 years 11 months ago
Training and Testing Low-degree Polynomial Data Mappings via Linear SVM
Kernel techniques have long been used in SVM to handle linearly inseparable problems by transforming data to a high dimensional space, but training and testing large data sets is ...
Yin-Wen Chang, Cho-Jui Hsieh, Kai-Wei Chang, Micha...
ECMAST
1998
Springer
118views Multimedia» more  ECMAST 1998»
15 years 8 months ago
Error-Robustness of Polar Contour-Coding
For communication using digital video, compression is mandatory because of the high bit-rate requiring a large bandwidth or storage capacity. Recent developments as well as upcomi...
Frank H. P. Spaan, Reginald L. Lagendijk, Jan Biem...
KDD
2002
ACM
118views Data Mining» more  KDD 2002»
16 years 4 months ago
SECRET: a scalable linear regression tree algorithm
Recently there has been an increasing interest in developing regression models for large datasets that are both accurate and easy to interpret. Regressors that have these properti...
Alin Dobra, Johannes Gehrke
ICASSP
2007
IEEE
15 years 10 months ago
Local Linear Discriminant Analysis (LLDA) for Inference of Multisubject FMRI Data
Large intersubject variability is a well-described feature of fMRI studies, making inter-group inference, of critical importance for biological interpretation, difficult. Therefor...
Martin J. McKeown, Junning Li, Xuemei Huang, Z. Ja...
BMCBI
2007
106views more  BMCBI 2007»
15 years 4 months ago
Improved classification accuracy in 1- and 2-dimensional NMR metabolomics data using the variance stabilising generalised logari
Background: Classifying nuclear magnetic resonance (NMR) spectra is a crucial step in many metabolomics experiments. Since several multivariate classification techniques depend up...
Helen M. Parsons, Christian Ludwig, Ulrich L. G&uu...