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» Privacy in Data Mining Using Formal Methods
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PAKDD
2000
ACM
161views Data Mining» more  PAKDD 2000»
15 years 10 months ago
Adaptive Boosting for Spatial Functions with Unstable Driving Attributes
Combining multiple global models (e.g. back-propagation based neural networks) is an effective technique for improving classification accuracy by reducing a variance through manipu...
Aleksandar Lazarevic, Tim Fiez, Zoran Obradovic
KDD
1999
ACM
199views Data Mining» more  KDD 1999»
15 years 11 months ago
The Application of AdaBoost for Distributed, Scalable and On-Line Learning
We propose to use AdaBoost to efficiently learn classifiers over very large and possibly distributed data sets that cannot fit into main memory, as well as on-line learning wher...
Wei Fan, Salvatore J. Stolfo, Junxin Zhang
CBMS
2008
IEEE
16 years 1 months ago
ProtCV: A Tool for Extracting, Visualizing and Validating Protein Clusters Using Mass Spectra Peak-Lists
We present ProtCV (Protein Clustering and Visualization) a new software tool for grouping samples (mass spectra peak-lists) emanating from a high throughput proteomics analysis ba...
Stavroula Ventoura, Eugenia G. Giannopoulou, Elias...
AFP
2008
Springer
236views Formal Methods» more  AFP 2008»
16 years 1 months ago
A Tutorial on Parallel and Concurrent Programming in Haskell
This practical tutorial introduces the features available in Haskell for writing parallel and concurrent programs. We first describe how to write semi-explicit parallel programs b...
Simon L. Peyton Jones, Satnam Singh
CCS
2008
ACM
15 years 8 months ago
The risk-utility tradeoff for IP address truncation
Network operators are reluctant to share traffic data due to security and privacy concerns. Consequently, there is a lack of publicly available traces for validating and generaliz...
Martin Burkhart, Daniela Brauckhoff, Martin May, E...