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SP
2008
IEEE
176views Security Privacy» more  SP 2008»
15 years 7 months ago
Casting out Demons: Sanitizing Training Data for Anomaly Sensors
The efficacy of Anomaly Detection (AD) sensors depends heavily on the quality of the data used to train them. Artificial or contrived training data may not provide a realistic v...
Gabriela F. Cretu, Angelos Stavrou, Michael E. Loc...
STORAGESS
2006
ACM
15 years 7 months ago
Using free web storage for data backup
Backing up important data is crucial. A variety of causes can lead to data loss, such as disk failures, administration errors, virus infiltration, theft, and physical damage to e...
Avishay Traeger, Nikolai Joukov, Josef Sipek, Erez...
ISBI
2002
IEEE
16 years 1 months ago
A new convergent MAP reconstruction algorithm for emission tomography using ordered subsets and separable surrogates
We investigate a new, fast and provably convergentMAP reconstruction algorithm for emission tomography. The new algorithm, termed C-OSEM has its origin in the alternating algorith...
Ing-Tsung Hsiao, Anand Rangarajan, Gene Gindi
KDD
2006
ACM
165views Data Mining» more  KDD 2006»
16 years 1 months ago
Training linear SVMs in linear time
Linear Support Vector Machines (SVMs) have become one of the most prominent machine learning techniques for highdimensional sparse data commonly encountered in applications like t...
Thorsten Joachims
PODS
2006
ACM
134views Database» more  PODS 2006»
16 years 1 months ago
Approximate quantiles and the order of the stream
Recently, there has been an increased focus on modeling uncertainty by distributions. Suppose we wish to compute a function of a stream whose elements are samples drawn independen...
Sudipto Guha, Andrew McGregor