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» Solving Consensus Using Structural Failure Models
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BMCBI
2008
132views more  BMCBI 2008»
14 years 11 months ago
Computational cluster validation for microarray data analysis: experimental assessment of Clest, Consensus Clustering, Figure of
Background: Inferring cluster structure in microarray datasets is a fundamental task for the so-called -omic sciences. It is also a fundamental question in Statistics, Data Analys...
Raffaele Giancarlo, Davide Scaturro, Filippo Utro
120
Voted
KDD
2009
ACM
198views Data Mining» more  KDD 2009»
16 years 6 days ago
Heterogeneous source consensus learning via decision propagation and negotiation
Nowadays, enormous amounts of data are continuously generated not only in massive scale, but also from different, sometimes conflicting, views. Therefore, it is important to conso...
Jing Gao, Wei Fan, Yizhou Sun, Jiawei Han
85
Voted
ISSRE
2003
IEEE
15 years 5 months ago
Using a Log-normal Failure Rate Distribution for Worst Case Bound Reliability Prediction
Prior research has suggested that the failure rates of faults follow a log normal distribution. We propose a specific model where distributions close to a log normal arise natural...
Peter G. Bishop, Robin E. Bloomfield
STOC
2002
ACM
112views Algorithms» more  STOC 2002»
15 years 12 months ago
Wait-free consensus with infinite arrivals
A randomized algorithm is given that solves the wait-free consensus problem for a shared-memory model with infinitely many processes. The algorithm is based on a weak shared coin ...
James Aspnes, Gauri Shah, Jatin Shah
80
Voted
STOC
1997
ACM
91views Algorithms» more  STOC 1997»
15 years 3 months ago
Lower Bounds for Distributed Coin-Flipping and Randomized Consensus
We examine a class of collective coin- ipping games that arises from randomized distributed algorithms with halting failures. In these games, a sequence of local coin ips is gener...
James Aspnes