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SIGKDD
2002
93views more  SIGKDD 2002»
15 years 6 months ago
Randomization in Privacy-Preserving Data Mining
Suppose there are many clients, each having some personal information, and one server, which is interested only in aggregate, statistically significant, properties of this informa...
Alexandre V. Evfimievski
CORR
2010
Springer
228views Education» more  CORR 2010»
15 years 5 months ago
Sparse Inverse Covariance Selection via Alternating Linearization Methods
Gaussian graphical models are of great interest in statistical learning. Because the conditional independencies between different nodes correspond to zero entries in the inverse c...
Katya Scheinberg, Shiqian Ma, Donald Goldfarb
GECCO
2006
Springer
162views Optimization» more  GECCO 2006»
15 years 11 months ago
Evolutionary learning with kernels: a generic solution for large margin problems
In this paper we embed evolutionary computation into statistical learning theory. First, we outline the connection between large margin optimization and statistical learning and s...
Ingo Mierswa
SIGMOD
2005
ACM
162views Database» more  SIGMOD 2005»
16 years 7 months ago
Fast and Approximate Stream Mining of Quantiles and Frequencies Using Graphics Processors
We present algorithms for fast quantile and frequency estimation in large data streams using graphics processor units (GPUs). We exploit the high computational power and memory ba...
Naga K. Govindaraju, Nikunj Raghuvanshi, Dinesh Ma...
GECCO
2007
Springer
174views Optimization» more  GECCO 2007»
16 years 1 months ago
Heuristic speciation for evolving neural network ensemble
Speciation is an important concept in evolutionary computation. It refers to an enhancements of evolutionary algorithms to generate a set of diverse solutions. The concept is stud...
Shin Ando