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FOCS
2006
IEEE
14 years 15 hour ago
Better lossless condensers through derandomized curve samplers
Lossless condensers are unbalanced expander graphs, with expansion close to optimal. Equivalently, they may be viewed as functions that use a short random seed to map a source on ...
Amnon Ta-Shma, Christopher Umans
ECCC
2006
77views more  ECCC 2006»
13 years 6 months ago
Extractors and condensers from univariate polynomials
We give new constructions of randomness extractors and lossless condensers that are optimal to within constant factors in both the seed length and the output length. For extractor...
Venkatesan Guruswami, Christopher Umans, Salil P. ...
EDBT
2004
ACM
234views Database» more  EDBT 2004»
14 years 6 months ago
A Condensation Approach to Privacy Preserving Data Mining
In recent years, privacy preserving data mining has become an important problem because of the large amount of personal data which is tracked by many business applications. In many...
Charu C. Aggarwal, Philip S. Yu
CAEPIA
2003
Springer
13 years 11 months ago
Using the Geometrical Distribution of Prototypes for Training Set Condensing
Abstract. In this paper, some new approaches to training set size reduction are presented. These schemes basically consist of defining a small number of prototypes that represent ...
María Teresa Lozano, José Salvador S...
ICPR
2006
IEEE
14 years 7 months ago
The Generalized Condensed Nearest Neighbor Rule as A Data Reduction Method
In this paper, we propose a new data reduction algorithm that iteratively selects some samples and ignores others that can be absorbed, or represented, by those selected. This alg...
Bo-Han Kuo, Chien-Hsing Chou, Fu Chang