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» Learning from Highly Structured Data by Decomposition
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ICPR
2004
IEEE
15 years 11 months ago
From Massively Parallel Image Processors to Fault-Tolerant Nanocomputers
Parallel processors such as SIMD computers have been successfully used in various areas of high performance image and data processing. Due to their characteristics of highly regula...
Jie Han, Pieter Jonker
77
Voted
CORR
2011
Springer
150views Education» more  CORR 2011»
14 years 5 months ago
Total variation regularization for fMRI-based prediction of behaviour
—While medical imaging typically provides massive amounts of data, the extraction of relevant information for predictive diagnosis remains a difficult challenge. Functional MRI ...
Vincent Michel, Alexandre Gramfort, Gaël Varo...
AAAI
2007
15 years 12 days ago
Isometric Projection
Recently the problem of dimensionality reduction has received a lot of interests in many fields of information processing. We consider the case where data is sampled from a low d...
Deng Cai, Xiaofei He, Jiawei Han
ENGL
2007
148views more  ENGL 2007»
14 years 10 months ago
A General Reflex Fuzzy Min-Max Neural Network
—“A General Reflex Fuzzy Min-Max Neural Network” (GRFMN) is presented. GRFMN is capable to extract the underlying structure of the data by means of supervised, unsupervised a...
Abhijeet V. Nandedkar, Prabir Kumar Biswas
RECOMB
2005
Springer
15 years 10 months ago
Predicting Transcription Factor Binding Sites Using Structural Knowledge
Abstract. Current approaches for identification and detection of transcription factor binding sites rely on an extensive set of known target genes. Here we describe a novel structu...
Tommy Kaplan, Nir Friedman, Hanah Margalit