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» Forecasting high-dimensional data
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ICAPR
2001
Springer
15 years 7 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
15 years 4 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
15 years 4 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
15 years 4 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»
15 years 2 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