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A Comparative Analysis of Methodologies for Database Schema Integration

by C. Batini, M. Lenzerini, S. B. Navathe - ACM COMPUTING SURVEYS , 1986
"... One of the fundamental principles of the database approach is that a database allows a nonredundant, unified representation of all data managed in an organization. This is achieved only when methodologies are available to support integration across organizational and application boundaries. Metho ..."
Abstract - Cited by 652 (10 self) - Add to MetaCart
schema. The aim of the paper is to provide first a unifying framework for the problem of schema integration, then a comparative review of the work done thus far in this area. Such a framework, with the associated analysis of the existing approaches, provides a basis for identifying strengths

Statistical pattern recognition: A review

by Anil K. Jain, Robert P. W. Duin, Jianchang Mao - IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE , 2000
"... The primary goal of pattern recognition is supervised or unsupervised classification. Among the various frameworks in which pattern recognition has been traditionally formulated, the statistical approach has been most intensively studied and used in practice. More recently, neural network techniques ..."
Abstract - Cited by 1035 (30 self) - Add to MetaCart
. New and emerging applications, such as data mining, web searching, retrieval of multimedia data, face recognition, and cursive handwriting recognition, require robust and efficient pattern recognition techniques. The objective of this review paper is to summarize and compare some of the well

Approximate Statistical Tests for Comparing Supervised Classification Learning Algorithms

by Thomas G. Dietterich , 1998
"... This article reviews five approximate statistical tests for determining whether one learning algorithm outperforms another on a particular learning task. These tests are compared experimentally to determine their probability of incorrectly detecting a difference when no difference exists (type I err ..."
Abstract - Cited by 723 (8 self) - Add to MetaCart
This article reviews five approximate statistical tests for determining whether one learning algorithm outperforms another on a particular learning task. These tests are compared experimentally to determine their probability of incorrectly detecting a difference when no difference exists (type I

A review of image denoising algorithms, with a new one

by A. Buades, B. Coll, J. M. Morel - SIMUL , 2005
"... The search for efficient image denoising methods is still a valid challenge at the crossing of functional analysis and statistics. In spite of the sophistication of the recently proposed methods, most algorithms have not yet attained a desirable level of applicability. All show an outstanding perf ..."
Abstract - Cited by 508 (6 self) - Add to MetaCart
performance when the image model corresponds to the algorithm assumptions but fail in general and create artifacts or remove image fine structures. The main focus of this paper is, first, to define a general mathematical and experimental methodology to compare and classify classical image denoising algorithms

Structural equation modeling in practice: a review and recommended two-step approach.

by James C Anderson , J L Kellogg , David W Gerbing , Jeanne Brett , Claes Fornell , David Larcker , William Perreault Jr , James Steiger - Psychological Bulletin, , 1988
"... In this article, we provide guidance for substantive researchers on the use of structural equation modeling in practice for theory testing and development. We present a comprehensive, two-step modeling approach that employs a series of nested models and sequential chi-square difference tests. We di ..."
Abstract - Cited by 1825 (3 self) - Add to MetaCart
discuss the comparative advantages of this approach over a one-step approach. Considerations in specification, assessment of fit, and respecification of measurement models using confirmatory factor analysis are reviewed. As background to the two-step approach, the distinction between exploratory

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
, and boosting. This paper reviews these methods and explains why ensembles can often perform better than any single classifier. Some previous studies comparing ensemble methods are reviewed, and some new experiments are presented to uncover the reasons that Adaboost does not overfit rapidly.

N: Meta-analysis in clinical trials

by Rebecca Dersimonian, Nan Laird - Controlled Clinical Trials , 1986
"... ABSTRACT: This paper examines eight published reviews each reporting results from several related trials. Each review pools the results from the relevant trials in order to evaluate the efficacy of a certain treatment for a specified medical condition. These reviews lack consistent assessment of hom ..."
Abstract - Cited by 1303 (0 self) - Add to MetaCart
ABSTRACT: This paper examines eight published reviews each reporting results from several related trials. Each review pools the results from the relevant trials in order to evaluate the efficacy of a certain treatment for a specified medical condition. These reviews lack consistent assessment

Selection of relevant features and examples in machine learning

by Avrim L. Blum, Pat Langley - ARTIFICIAL INTELLIGENCE , 1997
"... In this survey, we review work in machine learning on methods for handling data sets containing large amounts of irrelevant information. We focus on two key issues: the problem of selecting relevant features, and the problem of selecting relevant examples. We describe the advances that have been mad ..."
Abstract - Cited by 606 (2 self) - Add to MetaCart
In this survey, we review work in machine learning on methods for handling data sets containing large amounts of irrelevant information. We focus on two key issues: the problem of selecting relevant features, and the problem of selecting relevant examples. We describe the advances that have been

General methods for monitoring convergence of iterative simulations

by Stephen P. Brooksand, Andrew Gelman - J. Comput. Graph. Statist , 1998
"... We generalize the method proposed by Gelman and Rubin (1992a) for monitoring the convergence of iterative simulations by comparing between and within variances of multiple chains, in order to obtain a family of tests for convergence. We review methods of inference from simulations in order to develo ..."
Abstract - Cited by 551 (8 self) - Add to MetaCart
We generalize the method proposed by Gelman and Rubin (1992a) for monitoring the convergence of iterative simulations by comparing between and within variances of multiple chains, in order to obtain a family of tests for convergence. We review methods of inference from simulations in order

USER ACCEPTANCE OF INFORMATION TECHNOLOGY: TOWARD A UNIFIED VIEW

by Viswanath Venkatesh, Michael G. Morris, Gordon B. Davis, Fred D. Davis , 2003
"... Information technology (IT) acceptance research has yielded many competing models, each with different sets of acceptance determinants. In this paper, we (1) review user acceptance literature and discuss eight prominent models, (2) empirically compare the eight models and their extensions, (3) formu ..."
Abstract - Cited by 1807 (10 self) - Add to MetaCart
Information technology (IT) acceptance research has yielded many competing models, each with different sets of acceptance determinants. In this paper, we (1) review user acceptance literature and discuss eight prominent models, (2) empirically compare the eight models and their extensions, (3
Results 1 - 10 of 34,527