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» Approximation Methods for Supervised Learning
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ICDM
2008
IEEE
110views Data Mining» more  ICDM 2008»
15 years 8 months ago
Start Globally, Optimize Locally, Predict Globally: Improving Performance on Imbalanced Data
Class imbalance is a ubiquitous problem in supervised learning and has gained wide-scale attention in the literature. Perhaps the most prevalent solution is to apply sampling to t...
David A. Cieslak, Nitesh V. Chawla
MM
2003
ACM
84views Multimedia» more  MM 2003»
15 years 7 months ago
Temporal event clustering for digital photo collections
We present similarity-based methods to cluster digital photos by time and image content. The approach is general, unsupervised, and makes minimal assumptions regarding the structu...
Matthew L. Cooper, Jonathan Foote, Andreas Girgens...
IJON
2008
116views more  IJON 2008»
15 years 2 months ago
Discovering speech phones using convolutive non-negative matrix factorisation with a sparseness constraint
Discovering a representation that allows auditory data to be parsimoniously represented is useful for many machine learning and signal processing tasks. Such a representation can ...
Paul D. O'Grady, Barak A. Pearlmutter
BMCBI
2004
112views more  BMCBI 2004»
15 years 2 months ago
Predicting co-complexed protein pairs using genomic and proteomic data integration
Background: Identifying all protein-protein interactions in an organism is a major objective of proteomics. A related goal is to know which protein pairs are present in the same p...
Lan V. Zhang, Sharyl L. Wong, Oliver D. King, Fred...
IDEAL
2010
Springer
15 years 23 days ago
Dimension Reduction for Regression with Bottleneck Neural Networks
Dimension reduction for regression (DRR) deals with the problem of finding for high-dimensional data such low-dimensional representations, which preserve the ability to predict a ...
Elina Parviainen