119 search results - page 19 / 24 » Sparse kernel methods for high-dimensional survival data |
117
Voted
BMCBI
2010
15 years 3 months ago
2010
Background: Recent advances in proteomics technologies such as SELDI-TOF mass spectrometry has shown promise in the detection of early stage cancers. However, dimensionality reduc...
147
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UAI
2008
15 years 4 months ago
2008
Identifying co-varying causal elements in very high dimensional feature space with internal structures, e.g., a space with as many as millions of linearly ordered features, as one...
146
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MICCAI
2005
Springer
16 years 3 months ago
2005
Springer
Functional Magnetic Resonance Imaging(fMRI) has enabled scientists to look into the active human brain, leading to a flood of new data, thus encouraging the development of new data...
126
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ICML
2005
IEEE
16 years 3 months ago
2005
IEEE
The Relevance Vector Machine (RVM) is a sparse approximate Bayesian kernel method. It provides full predictive distributions for test cases. However, the predictive uncertainties ...
106
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NIPS
1998
15 years 4 months ago
1998
A common way to represent a time series is to divide it into shortduration blocks, each of which is then represented by a set of basis functions. A limitation of this approach, ho...