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Pairwise comparison

About: Pairwise comparison is a research topic. Over the lifetime, 6804 publications have been published within this topic receiving 174081 citations.


Papers
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Journal ArticleDOI
TL;DR: This work presents a practical method to approximate the probability that a local alignment score is a result of chance alone, and presents applications to data base searching and the analysis of pairwise and self-comparisons of proteins.
Abstract: A central question in sequence comparison is the statistical significance of an observed similarity. For local alignment containing gaps to optimize sequence similarity this problem has so far not been solved mathematically. Using as a basis the Chen-Stein theory of Poisson approximation, we present a practical method to approximate the probability that a local alignment score is a result of chance alone. For a set of similarity scores and gap penalties only one simulation of random alignments needs to be calculated to derive the key information allowing us to estimate the significance of any alignment calculated under this setting. We present applications to data base searching and the analysis of pairwise and self-comparisons of proteins.

154 citations

Journal ArticleDOI
TL;DR: This work presents a new pairwise model for graphical models with both continuous and discrete variables that is amenable to structure learning and involves a novel symmetric use of the group-lasso norm.
Abstract: We consider the problem of learning the structure of a pairwise graphical model over continuous and discrete variables. We present a new pairwise model for graphical models with both continuous and discrete variables that is amenable to structure learning. In previous work, authors have considered structure learning of Gaussian graphical models and structure learning of discrete models. Our approach is a natural generalization of these two lines of work to the mixed case. The penalization scheme involves a novel symmetric use of the group-lasso norm and follows naturally from a particular parameterization of the model. Supplementary materials for this article are available online.

154 citations

Journal ArticleDOI
TL;DR: The investigation proves the usefulness and strength of multiple comparison statistical procedures to analyse and select machine learning algorithms.
Abstract: In the paper we present some guidelines for the application of nonparametric statistical tests and post-hoc procedures devised to perform multiple comparisons of machine learning algorithms. We emphasize that it is necessary to distinguish between pairwise and multiple comparison tests. We show that the pairwise Wilcoxon test, when employed to multiple comparisons, will lead to overoptimistic conclusions. We carry out intensive normality examination employing ten different tests showing that the output of machine learning algorithms for regression problems does not satisfy normality requirements. We conduct experiments on nonparametric statistical tests and post-hoc procedures designed for multiple 1×N and N ×N comparisons with six different neural regression algorithms over 29 benchmark regression data sets. Our investigation proves the usefulness and strength of multiple comparison statistical procedures to analyse and select machine learning algorithms.

154 citations

Journal Article
TL;DR: In this paper, a pairwise censored likelihood is used for consistent estimation of the extremes of space-time data under mild mixing conditions, and illustrates this by fitting an extension of a model of Schlather (2002) to hourly rainfall data.
Abstract: Max-stable processes are the natural analogues of the generalized extreme-value distribution when modelling extreme events in space and time. Under suitable conditions, these processes are asymptotically justified models for maxima of independent replications of random fields, and they are also suitable for the modelling of extreme measurements over high thresholds. This paper shows how a pairwise censored likelihood can be used for consistent estimation of the extremes of space-time data under mild mixing conditions, and illustrates this by fitting an extension of a model of Schlather (2002) to hourly rainfall data. A block bootstrap procedure is used for uncertainty assessment. Estimator efficiency is considered and the choice of pairs to be included in the pairwise likelihood is discussed. The proposed model fits the data better than some natural competitors.

153 citations

Proceedings Article
01 Jan 1994
TL;DR: This work shows how to combine the outputs of the two-class neural networks in order to obtain posterior probabilities for the class decisions and presents results on real world data bases and shows that these results compare favorably to other neural network approaches.
Abstract: Multi-class classification problems can be efficiently solved by partitioning the original problem into sub-problems involving only two classes: for each pair of classes, a (potentially small) neural network is trained using only the data of these two classes. We show how to combine the outputs of the two-class neural networks in order to obtain posterior probabilities for the class decisions. The resulting probabilistic pairwise classifier is part of a handwriting recognition system which is currently applied to check reading. We present results on real world data bases and show that, from a practical point of view, these results compare favorably to other neural network approaches.

153 citations


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Performance
Metrics
No. of papers in the topic in previous years
YearPapers
20241
20231,305
20222,607
2021581
2020554
2019520