Meta Analysis of Classification Algorithms for Pattern Recognition

By S. Sohn. 1999

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Citations in the corpus

Statistical Meta-Analysis of Presentation Attacks for Secure Multibiometric Systems (2016, background): “, [32]); and (ii) uncertainty analysis, to evaluate the output of a system under unexpected input perturbations (see, e.”
Benchmark and Survey of Automated Machine Learning Frameworks (2019, methodology): “, (Sohn, 1999; Gama and Brazdil, 2000).”
Ranking Learning Algorithms: Using IBL and Meta-Learning on Accuracy and Time Results (2003, methodology): “Contrary to previous work using similar measures (Gama & Brazdil, 1995; Lindner & Studer, 1999; Bensusan & Kalousis, 2001; Kalousis & Theoharis, 1999; Sohn, 1999), we selected a set of measures a priori with the aim of representing some of the properties that affect the performance of algorithms.”
On the predictive power of meta-features in OpenML (2017, background): “Sohn (1999) notices that some of the characteristics are highly correlated, and she omits the redundant ones in her study.”
Taking Human out of Learning Applications: A Survey on Automated Machine Learning (2018, background): “The meta-learner is trained to map the meta-features to the performance [50], [99], [100], applicability [101], [102], or ranking [103], [104], [105], [106] of models.”
Meta-learning for evolutionary parameter optimization of classifiers (2012, background): “2003), or predicting the performance of algorithms individually (Gama and Brazdil 1995; Sohn 1999; Köpf et al. 2000; Bensusan and Kalousis 2001).”
Using Meta-Learning to Support Data Mining (2004, background): “Much work in dataset characterization has concentrated on extracting statistical and information-theoretic parameters estimated from the training set ([2],[21],[25],[31],[34],[46]).”
An experimental comparison of performance measures for classification (2009, background): “…many works have been devoted to study the resulting accuracy of an ensemble of combined classifiers given the original accuracies and some other conditions (Melnik et al., 2004; Narasimhamurthy, 2005; Kuncheva et al., 2003) or how to combine them in order to increase accuracy (Young Sohn, 1999).”
Measuring the complexity of classification problems (2000, background): “Comparative analysis of classifiers and how their performances relate to data characteristics has received attention only very recently [9].”
Estimating the Predictive Accuracy of a Classifier (2001, methodology): “Sohn in [19] uses the results of STATLOG (i.”
Complexity Measures of Supervised Classification Problems (2002, background): “Comparative analysis of classifiers that relate their performances to data characteristics has received attention only recently [21], [23].”