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» Forecasting high-dimensional data
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ICAPR
2001
Springer
15 years 2 months ago
Image Retrieval Using a Hierarchy of Clusters
The goal of this paper is to describe an efficient procedure for color-based image retrieval. The proposed procedure consists of two stages. First, the image data set is hierarchi...
Daniela Stan, Ishwar K. Sethi
ICONIP
2008
14 years 11 months ago
Improved Mass Spectrometry Peak Intensity Prediction by Adaptive Feature Weighting
Mass spectrometry (MS) is a key technique for the analysis and identification of proteins. A prediction of spectrum peak intensities from pre computed molecular features would pave...
Alexandra Scherbart, Wiebke Timm, Sebastian Bö...
NIPS
2007
14 years 11 months ago
SpAM: Sparse Additive Models
We present a new class of models for high-dimensional nonparametric regression and classification called sparse additive models (SpAM). Our methods combine ideas from sparse line...
Pradeep D. Ravikumar, Han Liu, John D. Lafferty, L...
IFIP12
2004
14 years 11 months ago
Introducing a Star Topology into Latent Class Models for Collaborative Filtering
Latent class models (LCM) represent the high dimensional data in a smaller dimensional space in terms of latent variables. They are able to automatically discover the patterns from...
Gabriela Polcicova, Peter Tiño
NECO
2000
190views more  NECO 2000»
14 years 9 months ago
Generalized Discriminant Analysis Using a Kernel Approach
We present a new method that we call Generalized Discriminant Analysis (GDA) to deal with nonlinear discriminant analysis using kernel function operator. The underlying theory is ...
G. Baudat, Fatiha Anouar