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66,247
Winner determination in voting trees with incomplete preferences and weighted votes
, 2012
"... ..."
An Empirical Comparison of Voting Classification Algorithms: Bagging, Boosting, and Variants
 MACHINE LEARNING
, 1999
"... 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 realworld datasets. We review these algorithms and describe a large empirical study comparing several variants in co ..."
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Cited by 695 (2 self)
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of probabilistic estimates, weight perturbations (Wagging), and
backfitting of data. We found that Bagging improves when
probabilistic estimates in conjunction with nopruning are used, as
well as when the data was backfit. We measure tree sizes and show
an interesting positive correlation between
Voting procedures with incomplete preferences
 in Proc. IJCAI05 Multidisciplinary Workshop on Advances in Preference Handling
, 2005
"... We extend the application of a voting procedure (usually defined on complete preference relations over candidates) when the voters ’ preferences consist of partial orders. We define possible (resp. necessary) winners for a given partial preference profile R with respect to a given voting procedure a ..."
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Cited by 93 (10 self)
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We extend the application of a voting procedure (usually defined on complete preference relations over candidates) when the voters ’ preferences consist of partial orders. We define possible (resp. necessary) winners for a given partial preference profile R with respect to a given voting procedure
Improved algorithms for optimal winner determination in combinatorial auctions and generalizations
, 2000
"... Combinatorial auctions can be used to reach efficient resource and task allocations in multiagent systems where the items are complementary. Determining the winners is NPcomplete and inapproximable, but it was recently shown that optimal search algorithms do very well on average. This paper present ..."
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Cited by 598 (55 self)
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presents a more sophisticated search algorithm for optimal (and anytime) winner determination, including structural improvements that reduce search tree size, faster data structures, and optimizations at search nodes based on driving toward, identifying and solving tractable special cases. We also uncover
Winner determination in sequential majority voting
 In Proceedings of the ECAI2006 Multidisciplinary Workshop on Advances in Preference Handling
, 2007
"... Preferences can be aggregated using a voting rule. Each agent gives their preference orderings over a set of candidates, and a voting rule is used to compute the winner. We consider voting rules which perform a sequence of pairwise comparisons between two candidates, where the result of each is comp ..."
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Cited by 45 (14 self)
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winners, we prove that it is difficult if we insist that the tree is balanced. This restriction is therefore enough to make voting difficult for the chair to manipulate. We also consider the situation where we lack complete informations about preferences, and determine the computational complexity
An Efficient Boosting Algorithm for Combining Preferences
, 1999
"... The problem of combining preferences arises in several applications, such as combining the results of different search engines. This work describes an efficient algorithm for combining multiple preferences. We first give a formal framework for the problem. We then describe and analyze a new boosting ..."
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Cited by 707 (18 self)
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The problem of combining preferences arises in several applications, such as combining the results of different search engines. This work describes an efficient algorithm for combining multiple preferences. We first give a formal framework for the problem. We then describe and analyze a new
Locally weighted learning
 ARTIFICIAL INTELLIGENCE REVIEW
, 1997
"... This paper surveys locally weighted learning, a form of lazy learning and memorybased learning, and focuses on locally weighted linear regression. The survey discusses distance functions, smoothing parameters, weighting functions, local model structures, regularization of the estimates and bias, ass ..."
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Cited by 594 (53 self)
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This paper surveys locally weighted learning, a form of lazy learning and memorybased learning, and focuses on locally weighted linear regression. The survey discusses distance functions, smoothing parameters, weighting functions, local model structures, regularization of the estimates and bias
Voting
"... This document consists of two modules. The first studies a variety of multicandidate voting systems, including approval, Borda, and cumulative, voting, using a model which takes account of a voter's intensity ot preference for candidates. The voter's optimal strategy is investigated for ea ..."
Abstract
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This document consists of two modules. The first studies a variety of multicandidate voting systems, including approval, Borda, and cumulative, voting, using a model which takes account of a voter's intensity ot preference for candidates. The voter's optimal strategy is investigated
Empirical Analysis of Predictive Algorithm for Collaborative Filtering
 Proceedings of the 14 th Conference on Uncertainty in Artificial Intelligence
, 1998
"... 1 ..."
Wrappers for Feature Subset Selection
 AIJ SPECIAL ISSUE ON RELEVANCE
, 1997
"... In the feature subset selection problem, a learning algorithm is faced with the problem of selecting a relevant subset of features upon which to focus its attention, while ignoring the rest. To achieve the best possible performance with a particular learning algorithm on a particular training set, a ..."
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Cited by 1522 (3 self)
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families of induction algorithms used: decision trees and NaiveBayes.
Results 1  10
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66,247