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» Approximate data mining in very large relational data
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KDD
1998
ACM
141views Data Mining» more  KDD 1998»
15 years 8 months ago
Rule Discovery from Time Series
We consider the problem of nding rules relating patterns in a time series to other patterns in that series, or patterns in one series to patterns in another series. A simple examp...
Gautam Das, King-Ip Lin, Heikki Mannila, Gopal Ren...
NIPS
2001
15 years 5 months ago
Laplacian Eigenmaps and Spectral Techniques for Embedding and Clustering
Drawing on the correspondence between the graph Laplacian, the Laplace-Beltrami operator on a manifold, and the connections to the heat equation, we propose a geometrically motiva...
Mikhail Belkin, Partha Niyogi
CVPR
2005
IEEE
16 years 6 months ago
Learning a Similarity Metric Discriminatively, with Application to Face Verification
We present a method for training a similarity metric from data. The method can be used for recognition or verification applications where the number of categories is very large an...
Sumit Chopra, Raia Hadsell, Yann LeCun
ICCAD
2006
IEEE
125views Hardware» more  ICCAD 2006»
16 years 1 months ago
Leveraging protocol knowledge in slack matching
Stalls, due to mis-matches in communication rates, are a major performance obstacle in pipelined circuits. If the rate of data production is faster than the rate of consumption, t...
Girish Venkataramani, Seth Copen Goldstein
ICASSP
2009
IEEE
15 years 2 months ago
Weighted nonnegative matrix factorization
Nonnegative matrix factorization (NMF) is a widely-used method for low-rank approximation (LRA) of a nonnegative matrix (matrix with only nonnegative entries), where nonnegativity...
Yong-Deok Kim, Seungjin Choi