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COLT
2005
Springer
16 years 1 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»
15 years 12 months 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 8 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...
STOC
2006
ACM
116views Algorithms» more  STOC 2006»
16 years 6 months ago
Linear degree extractors and the inapproximability of max clique and chromatic number
: We derandomize results of H?astad (1999) and Feige and Kilian (1998) and show that for all > 0, approximating MAX CLIQUE and CHROMATIC NUMBER to within n1are NP-hard. We furt...
David Zuckerman
KBSE
2007
IEEE
16 years 23 days ago
Nighthawk: a two-level genetic-random unit test data generator
Randomized testing has been shown to be an effective method for testing software units. However, the thoroughness of randomized unit testing varies widely according to the settin...
James H. Andrews, Felix Chun Hang Li, Tim Menzies