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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

Attention, similarity, and the identification-Categorization Relationship

by Robert M. Nosofsky , 1986
"... A unified quantitative approach to modeling subjects ' identification and categorization of multidimensional perceptual stimuli is proposed and tested. Two subjects identified and categorized the same set of perceptually confusable stimuli varying on separable dimensions. The identification dat ..."
Abstract - Cited by 690 (28 self) - Add to MetaCart
, because of the influence of selective attention, similarity relationships change systematically across the two paradigms. Some support was gained for the hypothesis that subjects distribute attention among component dimensions so as to optimize categorization performance. Evidence was also obtained

Loopy belief propagation for approximate inference: An empirical study. In:

by Kevin P Murphy , Yair Weiss , Michael I Jordan - Proceedings of Uncertainty in AI, , 1999
"... Abstract Recently, researchers have demonstrated that "loopy belief propagation" -the use of Pearl's polytree algorithm in a Bayesian network with loops -can perform well in the context of error-correcting codes. The most dramatic instance of this is the near Shannon-limit performanc ..."
Abstract - Cited by 676 (15 self) - Add to MetaCart
;belief revision") version, Weiss For the case of networks with multiple loops, Richard son To summarize, what is currently known about loopy propagation is that ( 1) it works very well in an error correcting code setting and (2) there are conditions for a single-loop network for which it can be guaranteed

Benchmarking Least Squares Support Vector Machine Classifiers

by Tony Van Gestel, Johan A. K. Suykens, Bart Baesens, Stijn Viaene, Jan Vanthienen, Guido Dedene, Bart De Moor, Joos Vandewalle - NEURAL PROCESSING LETTERS , 2001
"... In Support Vector Machines (SVMs), the solution of the classification problem is characterized by a (convex) quadratic programming (QP) problem. In a modified version of SVMs, called Least Squares SVM classifiers (LS-SVMs), a least squares cost function is proposed so as to obtain a linear set of eq ..."
Abstract - Cited by 476 (46 self) - Add to MetaCart
(RBF) kernels. Both the SVM and LS-SVM classifier with RBF kernel in combination with standard cross-validation procedures for hyperparameter selection achieve comparable test set performances. These SVM and LS-SVM performances are consistently very good when compared to a variety of methods described

A Safe, Efficient Regression Test Selection Technique

by Gregg Rothermel , Mary Jean Harrold - ACM TRANSACTIONS ON SOFTWARE ENGINEERING AND METHODOLOGY , 1997
"... Regression testing is an expensive but necessary maintenance activity performed on modified software to provide confidence that changes are correct and do not adversely affect other portions of the software. A regression test selection technique chooses, from an existing test set, tests that are d ..."
Abstract - Cited by 227 (65 self) - Add to MetaCart
that are deemed necessary to validate modified software. We present a new technique for regression test selection. Our algorithms construct control flow graphs for a procedure or program and its modified version, and use these graphs to select tests that execute changed code from the original test suite. We

Regression Test Selection for Java Software

by Mary Jean Harrold, Alessandro Orso, James A. Jones, Maikel Pennings, Tongyu Li, Ashish Gujarathi, Saurabh Sinha, Donglin Liang, S. Alexander Spoon , 2001
"... Regression testing is applied to modified software to provide confidence that the changed parts behave as intended and that the unchanged parts have not been adversely affected by the modifications. To reduce the cost of regression testing, test cases are selected from the test suite that was used t ..."
Abstract - Cited by 99 (12 self) - Add to MetaCart
Regression testing is applied to modified software to provide confidence that the changed parts behave as intended and that the unchanged parts have not been adversely affected by the modifications. To reduce the cost of regression testing, test cases are selected from the test suite that was used

TestTube: A System for Selective Regression Testing

by Yih-farn Chen, David S. Rosenblum, Kiem-phong Vo - In Proceedings of the 16th International Conference on Software Engineering , 1994
"... This paper describes a system called TestTube that combines static and dynamic analysis to perform selective retesting of software systems written in C. TestTube first identifies which functions, types, variables and macros are covered by each test unit in a test suite. Each time the system under t ..."
Abstract - Cited by 151 (13 self) - Add to MetaCart
test is modified, TestTube identifies which entities were changed to create the new version. Using the coverage and change information, TestTube selects only those test units that cover the changed entities for testing the new version. We have applied TestTube to selective retesting of two software

Applying regression test selection for cots-based applications

by Jiang Zheng, Brian Robinson, Laurie Williams, Karen Smiley - In proceedings of the International Conference on Software Engineering. ACM , 2006
"... ABB incorporates a variety of commercial-off-the-shelf (COTS) components in its products. When new releases of these components are made available for integration and testing, source code is often not provided. Various regression test selection processes have been developed and have been shown to be ..."
Abstract - Cited by 16 (3 self) - Add to MetaCart
to be cost effectiveness. However, the majority of these test selection techniques rely on access to source code for change identification. In this paper we present the application of the lightweight Integrated- Black-box Approach for Component Change Identification (I-BACCI) Version 3 process that select

A Study of Effective Regression Testing in Practice

by W. Eric Wong, J. R. Horgan, Saul London, Hira Agrawal - In Proceedings of the Eighth International Symposium on Software Reliability Engineering , 1997
"... The purpose of regression testing is to ensure that changes made to software, such as adding new features or modifying existing features, have not adversely affected features of the software that should not change. Regression testing is usually performed by running some, or all, of the test cases cr ..."
Abstract - Cited by 110 (1 self) - Add to MetaCart
created to test modifications in previous versions of the software. Many techniques have been reported on how to select regression tests so that the number of test cases does not grow too large as the software evolves. Our proposed hybrid technique combines modification, minimization and prioritization-based

The Representation and Recognition of Action Using Temporal Templates

by James Davis, Aaron Bobick , 1997
"... A new view-based approach to the representation and recognition of action is presented. The basis of the representation is a temporal template --- a static vector-image where the vector value at each point is a function of the motion properties at the corresponding spatial location in an image seque ..."
Abstract - Cited by 235 (10 self) - Add to MetaCart
sequence. Using 18 aerobics exercises as a test domain, we explore the representational power of a simple, two component version of the templates: the #rst value is a binary value indicating the presence of motion, and the second value is a function of the recency of motion in a sequence. We then develop a
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