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» Randomized Algorithms on the Mesh
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STOC
2006
ACM
244views Algorithms» more  STOC 2006»
16 years 7 months ago
Approximate nearest neighbors and the fast Johnson-Lindenstrauss transform
We introduce a new low-distortion embedding of d 2 into O(log n) p (p = 1, 2), called the Fast-Johnson-LindenstraussTransform. The FJLT is faster than standard random projections ...
Nir Ailon, Bernard Chazelle
KDD
2002
ACM
128views Data Mining» more  KDD 2002»
16 years 7 months ago
Privacy preserving mining of association rules
We present a framework for mining association rules from transactions consisting of categorical items where the data has been randomized to preserve privacy of individual transact...
Alexandre V. Evfimievski, Ramakrishnan Srikant, Ra...
182
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COLT
2005
Springer
16 years 11 days ago
Separating Models of Learning from Correlated and Uncorrelated Data
We consider a natural framework of learning from correlated data, in which successive examples used for learning are generated according to a random walk over the space of possibl...
Ariel Elbaz, Homin K. Lee, Rocco A. Servedio, Andr...
DMKD
2004
ACM
136views Data Mining» more  DMKD 2004»
16 years 8 days ago
Mining association rules with non-uniform privacy concerns
Privacy concerns have become an important issue in data mining. A popular way to preserve privacy is to randomize the dataset to be mined in a systematic way and mine the randomiz...
Yi Xia, Yirong Yang, Yun Chi
AAAI
2008
15 years 9 months ago
Constrained Classification on Structured Data
Most standard learning algorithms, such as Logistic Regression (LR) and the Support Vector Machine (SVM), are designed to deal with i.i.d. (independent and identically distributed...
Chi-Hoon Lee, Matthew R. G. Brown, Russell Greiner...