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Experiments with classifier combination rules
- in Proc Workshop. Multiple Classifier Systems
"... Abstract. A large experiment on combining classifiers is reported and discussed. It includes, both, the combination of different classifiers on the same feature set and the combination of classifiers on different feature sets. Various fixed and trained combining rules are used. It is shown that ther ..."
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Cited by 54 (1 self)
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Abstract. A large experiment on combining classifiers is reported and discussed. It includes, both, the combination of different classifiers on the same feature set and the combination of classifiers on different feature sets. Various fixed and trained combining rules are used. It is shown
On combining classifiers
- IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE
, 1998
"... We develop a common theoretical framework for combining classifiers which use distinct pattern representations and show that many existing schemes can be considered as special cases of compound classification where all the pattern representations are used jointly to make a decision. An experimental ..."
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Cited by 1392 (32 self)
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. An experimental comparison of various classifier combination schemes demonstrates that the combination rule developed under the most restrictive assumptions—the sum rule—outperforms other classifier combinations schemes. A sensitivity analysis of the various schemes to estimation errors is carried out to show
The SDL Combination Rule of Evidence
, 2004
"... Abstract – In this paper one proposes a new simple combination rule, similar to Dempster’s rule, but the normalization is done for each set with respect to the non-zero sum of its corresponding mass matrix. A general formula is proposed with several numerical examples and comparisons with other rule ..."
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Abstract – In this paper one proposes a new simple combination rule, similar to Dempster’s rule, but the normalization is done for each set with respect to the non-zero sum of its corresponding mass matrix. A general formula is proposed with several numerical examples and comparisons with other
An Algebraic View of Combination Rules
, 1994
"... . Combining information from multiple sources is a topic of central importance for Uncertain Reasoning. In this paper we approach the problem of formulating appropriate Combination Rules for Uncertain Reasoning from an algebraic, lattice-theoretical perspective. Our purpose is to state the problem o ..."
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Cited by 1 (1 self)
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. Combining information from multiple sources is a topic of central importance for Uncertain Reasoning. In this paper we approach the problem of formulating appropriate Combination Rules for Uncertain Reasoning from an algebraic, lattice-theoretical perspective. Our purpose is to state the problem
Adapting a combination rule to . . .
, 2008
"... In this article, we address the combination of non-independent sources to solve classification problems, within the theory of belief functions. We show that the cautious rule of combination [1, 2] is well-suited to such problems. We propose a method to learn the combination rule from training data, ..."
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In this article, we address the combination of non-independent sources to solve classification problems, within the theory of belief functions. We show that the cautious rule of combination [1, 2] is well-suited to such problems. We propose a method to learn the combination rule from training data
Fast Algorithms for Mining Association Rules
, 1994
"... We consider the problem of discovering association rules between items in a large database of sales transactions. We present two new algorithms for solving this problem that are fundamentally different from the known algorithms. Empirical evaluation shows that these algorithms outperform the known a ..."
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Cited by 3551 (15 self)
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We consider the problem of discovering association rules between items in a large database of sales transactions. We present two new algorithms for solving this problem that are fundamentally different from the known algorithms. Empirical evaluation shows that these algorithms outperform the known
Rules, discretion, and reputation in a model of monetary policy
- JOURNAL OF MONETARY ECONOMICS
, 1983
"... In a discretionary regime the monetary authority can print more money and create more inflation than people expect. But, although these inflation surprises can have some benefits, they cannot arise systematically in equilibrium when people understand the policymakor's incentives and form their ..."
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Cited by 794 (9 self)
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their expectations accordingly. Because the policymaker has the power to create inflation shocks ex post, the equilibrium growth rates of money and prices turn out to be higher than otherwise. Therefore, enforced commitments (rules) for monetary behavior can improve matters. Given the repeated interaction between
Training Products of Experts by Minimizing Contrastive Divergence
, 2002
"... It is possible to combine multiple latent-variable models of the same data by multiplying their probability distributions together and then renormalizing. This way of combining individual “expert ” models makes it hard to generate samples from the combined model but easy to infer the values of the l ..."
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Cited by 829 (79 self)
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of the latent variables of each expert, because the combination rule ensures that the latent variables of different experts are conditionally independent when given the data. A product of experts (PoE) is therefore an interesting candidate for a perceptual system in which rapid inference is vital and generation
The role of combining rules in bagging and boosting
- In: Ferri FJ, Inesta JM, Amin A, Pudil P (eds) Advances in Pattern Recognition (Proceedings Joint International Workshops SSPR’2000 and SPR’2000
, 2000
"... Abstract. To improve weak classifiers bagging and boosting could be used. These techniques are based on combining classifiers. Usually, a simple majority vote or a weighted majority vote are used as combining rules in bagging and boosting. However, other combining rules such as mean, product and ave ..."
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Cited by 4 (2 self)
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Abstract. To improve weak classifiers bagging and boosting could be used. These techniques are based on combining classifiers. Usually, a simple majority vote or a weighted majority vote are used as combining rules in bagging and boosting. However, other combining rules such as mean, product
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
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