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Weighted Voting for Replicated Data

by David K. Gifford , 1979
"... In a new algorithm for maintaining replicated data, every copy of a replicated file is assigned some number of votes. Every transaction collects a read quorum of r votes to read a file, and a write quorum of w votes to write a file, such that r+w is greater than the total number number of votes assi ..."
Abstract - Cited by 598 (0 self) - Add to MetaCart
In a new algorithm for maintaining replicated data, every copy of a replicated file is assigned some number of votes. Every transaction collects a read quorum of r votes to read a file, and a write quorum of w votes to write a file, such that r+w is greater than the total number number of votes

The weighted majority algorithm

by Nick Littlestone, Manfred K. Warmuth , 1992
"... We study the construction of prediction algorithms in a situation in which a learner faces a sequence of trials, with a prediction to be made in each, and the goal of the learner is to make few mistakes. We are interested in the case that the learner has reason to believe that one of some pool of kn ..."
Abstract - Cited by 877 (43 self) - Add to MetaCart
of known algorithms will perform well, but the learner does not know which one. A simple and effective method, based on weighted voting, is introduced for constructing a compound algorithm in such a circumstance. We call this method the Weighted Majority Algorithm. We show that this algorithm is robust

An empirical comparison of voting classification algorithms: Bagging, boosting, and variants.

by Eric Bauer , Philip Chan , Salvatore Stolfo , David Wolpert - Machine Learning, , 1999
"... Abstract. Methods for voting classification algorithms, such as Bagging and AdaBoost, have been shown to be very successful in improving the accuracy of certain classifiers for artificial and real-world datasets. We review these algorithms and describe a large empirical study comparing several vari ..."
Abstract - Cited by 707 (2 self) - Add to MetaCart
estimates, weight perturbations (Wagging), and backfitting of data. We found that Bagging improves when probabilistic estimates in conjunction with no-pruning are used, as well as when the data was backfit. We measure tree sizes and show an interesting positive correlation between the increase

Ensemble Methods in Machine Learning

by Thomas G. Dietterich - MULTIPLE CLASSIFIER SYSTEMS, LBCS-1857 , 2000
"... Ensemble methods are learning algorithms that construct a set of classifiers and then classify new data points by taking a (weighted) vote of their predictions. The original ensemble method is Bayesian averaging, but more recent algorithms include error-correcting output coding, Bagging, and boostin ..."
Abstract - Cited by 625 (3 self) - Add to MetaCart
Ensemble methods are learning algorithms that construct a set of classifiers and then classify new data points by taking a (weighted) vote of their predictions. The original ensemble method is Bayesian averaging, but more recent algorithms include error-correcting output coding, Bagging

Bagging predictors

by LEO BREIMAN , 1996
"... Bagging predictors is a method for generating multiple versions of a predictor and using these to get an aggregated predictor. The aggregation averages over the versions when predicting a numerical outcome and does a plurality vote when predicting a class. The multiple versions are formed by making ..."
Abstract - Cited by 3650 (1 self) - Add to MetaCart
by making bootstrap replicates of the learning set and using these as new learning sets. Tests on real and simulated data sets using classification and regression trees and subset selection in linear regression show that bagging can give substantial gains in accuracy. The vital element is the instability

Additive Logistic Regression: a Statistical View of Boosting

by Jerome Friedman, Trevor Hastie, Robert Tibshirani - Annals of Statistics , 1998
"... Boosting (Freund & Schapire 1996, Schapire & Singer 1998) is one of the most important recent developments in classification methodology. The performance of many classification algorithms can often be dramatically improved by sequentially applying them to reweighted versions of the input dat ..."
Abstract - Cited by 1750 (25 self) - Add to MetaCart
data, and taking a weighted majority vote of the sequence of classifiers thereby produced. We show that this seemingly mysterious phenomenon can be understood in terms of well known statistical principles, namely additive modeling and maximum likelihood. For the two-class problem, boosting can

Linear models and empirical bayes methods for assessing differential expression in microarray experiments.

by Gordon K Smyth , Gordon K Smyth - Stat. Appl. Genet. Mol. Biol. , 2004
"... Abstract The problem of identifying differentially expressed genes in designed microarray experiments is considered. Lonnstedt and Speed (2002) derived an expression for the posterior odds of differential expression in a replicated two-color experiment using a simple hierarchical parametric model. ..."
Abstract - Cited by 1321 (24 self) - Add to MetaCart
Abstract The problem of identifying differentially expressed genes in designed microarray experiments is considered. Lonnstedt and Speed (2002) derived an expression for the posterior odds of differential expression in a replicated two-color experiment using a simple hierarchical parametric model

Bagging, Boosting, and C4.5

by J. R. Quinlan - In Proceedings of the Thirteenth National Conference on Artificial Intelligence , 1996
"... Breiman's bagging and Freund and Schapire's boosting are recent methods for improving the predictive power of classifier learning systems. Both form a set of classifiers that are combined by voting, bagging by generating replicated bootstrap samples of the data, and boosting by adjusting ..."
Abstract - Cited by 326 (1 self) - Add to MetaCart
Breiman's bagging and Freund and Schapire's boosting are recent methods for improving the predictive power of classifier learning systems. Both form a set of classifiers that are combined by voting, bagging by generating replicated bootstrap samples of the data, and boosting by adjusting

Arcing Classifiers

by Leo Breiman , 1998
"... Recent work has shown that combining multiple versions of unstable classifiers such as trees or neural nets results in reduced test set error. One of the more effective is bagging (Breiman [1996a] ) Here, modified training sets are formed by resampling from the original training set, classifiers con ..."
Abstract - Cited by 345 (6 self) - Add to MetaCart
constructed using these training sets and then combined by voting. Freund and Schapire [1995,1996] propose an algorithm the basis of which is to adaptively resample and combine (hence the acronym--arcing) so that the weights in the resampling are increased for those cases most often misclassified

A weighted voting algorithm for replicated directories

by Joshua J. Bloch, Dean S. Daniel, Alfred, Z. Spector - JACM , 1987
"... Abstract. Weighted voting is used as the basis for a replication technique for directories. This technique affords arbitrarily high data availability as well as high concurrency. Efficient algorithms are presented for all of the standard directory operations. A structural property of the replicated ..."
Abstract - Cited by 19 (2 self) - Add to MetaCart
Abstract. Weighted voting is used as the basis for a replication technique for directories. This technique affords arbitrarily high data availability as well as high concurrency. Efficient algorithms are presented for all of the standard directory operations. A structural property of the replicated
Results 1 - 10 of 1,158