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FOCS
2003
IEEE
15 years 9 months ago
Learning DNF from Random Walks
We consider a model of learning Boolean functions from examples generated by a uniform random walk on {0, 1}n . We give a polynomial time algorithm for learning decision trees and...
Nader H. Bshouty, Elchanan Mossel, Ryan O'Donnell,...
SODA
2008
ACM
110views Algorithms» more  SODA 2008»
15 years 5 months ago
Why simple hash functions work: exploiting the entropy in a data stream
Hashing is fundamental to many algorithms and data structures widely used in practice. For theoretical analysis of hashing, there have been two main approaches. First, one can ass...
Michael Mitzenmacher, Salil P. Vadhan
ECCC
2010
103views more  ECCC 2010»
15 years 3 months ago
Time-Space Efficient Simulations of Quantum Computations
We give two time- and space-efficient simulations of quantum computations with intermediate measurements, one by classical randomized computations with unbounded error and the oth...
Dieter van Melkebeek, Thomas Watson
99
Voted
IJON
2008
118views more  IJON 2008»
15 years 3 months ago
Incremental extreme learning machine with fully complex hidden nodes
Huang et al. [Universal approximation using incremental constructive feedforward networks with random hidden nodes, IEEE Trans. Neural Networks 17(4) (2006) 879
Guang-Bin Huang, Ming-Bin Li, Lei Chen, Chee Kheon...
PKDD
2005
Springer
101views Data Mining» more  PKDD 2005»
15 years 9 months ago
A Random Method for Quantifying Changing Distributions in Data Streams
In applications such as fraud and intrusion detection, it is of great interest to measure the evolving trends in the data. We consider the problem of quantifying changes between tw...
Haixun Wang, Jian Pei