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Theoretical analyses of cross-validation error and voting in instance-based learning

PETER TURNEY
<span title="">1994</span> <i title="Informa UK Limited"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/t5mpusqpirhnjlrcurxdscrpl4" style="color: black;">Journal of experimental and theoretical artificial intelligence (Print)</a> </i> &nbsp;
This paper begins with a general theory of error in cross-validation testing of algorithms for supervised learning from examples.  ...  This general theory is then applied to voting in instance-based learning.  ...  I would also like to thank Professor Malcolm Forster for pointing out the relation between my work and Akaike Information Criterion statistics.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1080/09528139408953793">doi:10.1080/09528139408953793</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/melkxgjmpneyzj5i6sgxcecemu">fatcat:melkxgjmpneyzj5i6sgxcecemu</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20170808214445/http://cogprints.org/1821/3/NRC-35073.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/13/58/1358cd06ec922ba121b83cceda815a4156ed685f.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1080/09528139408953793"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="external alternate icon"></i> tandfonline.com </button> </a>

Rolling Element Bearing Fault Detection using Statistical Features and Ensemble Classifiers

<span title="2020-02-29">2020</span> <i title="Blue Eyes Intelligence Engineering and Sciences Engineering and Sciences Publication - BEIESP"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/h673cvfolnhl3mnbjxkhtxdtg4" style="color: black;">International Journal of Engineering and Advanced Technology</a> </i> &nbsp;
Various machine learning techniques have been used for interpretation and accurate fault diagnosis using this extracted feature set.  ...  A stacked ensemble of five classifiers is proposed for accurate fault diagnosis and results are compared with conventional ensemble classifiers to prove its effectiveness  ...  TP-True positive, FP-False positive, FN-False negative and TN-True negative Mean Absolute Error (MAE): Sum of absolute errors for all instances divided by the number of instances is called mean absolute  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.35940/ijeat.c4836.029320">doi:10.35940/ijeat.c4836.029320</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/hzrte7kly5eihlmhs5m6e6ky3i">fatcat:hzrte7kly5eihlmhs5m6e6ky3i</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20200307045056/https://www.ijeat.org/wp-content/uploads/papers/v9i3/C4836029320.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.35940/ijeat.c4836.029320"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="unlock alternate icon" style="background-color: #fb971f;"></i> Publisher / doi.org </button> </a>

An Informed Forensics Approach to Detecting Vote Irregularities

Jacob M. Montgomery, Santiago Olivella, Joshua D. Potter, Brian F. Crisp
<span title="">2015</span> <i title="Cambridge University Press (CUP)"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/7ysuj5jirzc5dkdoksxiwc72py" style="color: black;">Political Analysis</a> </i> &nbsp;
easily implemented in cross-national research.  ...  We deploy a Bayesian additive regression trees (BART) model–a machine-learning technique–on a large cross-national data set to explore the dense network of potential relationships between various forensic  ...  That is, cross validation and bootstrap methods designed to infer the error rate of a model independent of a specific training sample are generally inaccurate.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1093/pan/mpv023">doi:10.1093/pan/mpv023</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/klbg5b57ivayffcczxqvuo35xm">fatcat:klbg5b57ivayffcczxqvuo35xm</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20170706040427/http://pages.wustl.edu/montgomery/electoralfraud" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/b5/fd/b5fd4ff577453d6e56734878b9a2bed23ee790a5.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1093/pan/mpv023"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="external alternate icon"></i> Publisher / doi.org </button> </a>

When does Diversity Help Generalization in Classification Ensembles? [article]

Yijun Bian, Huanhuan Chen
<span title="2021-01-19">2021</span> <i > arXiv </i> &nbsp; <span class="release-stage" >pre-print</span>
Empirical results validate the reasonableness of the proposed relationship between diversity and ensemble generalization error and the effectiveness of the proposed pruning methods.  ...  Ensembles, as a widely used and effective technique in the machine learning community, succeed within a key element -- "diversity."  ...  METHODOLOGY In this section, we formally study the measure of diversity using error decomposition of classification ensembles and derive proper theoretical analyses from quantifying and characterizing  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/1910.13631v2">arXiv:1910.13631v2</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/zj7stoyjvncgvhpzho6gajvfge">fatcat:zj7stoyjvncgvhpzho6gajvfge</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20200905025947/https://arxiv.org/pdf/1910.13631v1.pdf" title="fulltext PDF download [not primary version]" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <span style="color: #f43e3e;">&#10033;</span> <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/3e/fe/3efebb72ff55a86093e29ad38d0da1fe38a9691d.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/1910.13631v2" title="arxiv.org access"> <button class="ui compact blue labeled icon button serp-button"> <i class="file alternate outline icon"></i> arxiv.org </button> </a>

A Systematic Review of Unsupervised Learning Techniques for Software Defect Prediction [article]

Ning Li, Martin Shepperd, Yuchen Guo
<span title="2020-02-19">2020</span> <i > arXiv </i> &nbsp; <span class="release-stage" >pre-print</span>
Objective: Investigate the use and performance of unsupervised learning techniques in software defect prediction.  ...  In addition, where we were able to check, we found that almost 11% (262/2456) of published results (contained in 16 papers) were internally inconsistent and a further 33% (823/2456) provided insufficient  ...  We also wish to acknowledge the use of the DConfusion tool developed by David Bowes and  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/1907.12027v4">arXiv:1907.12027v4</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/q2o5ew5zhra5lauyebd3hl65uy">fatcat:q2o5ew5zhra5lauyebd3hl65uy</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20200321074211/https://arxiv.org/pdf/1907.12027v4.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] </button> </a> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/1907.12027v4" title="arxiv.org access"> <button class="ui compact blue labeled icon button serp-button"> <i class="file alternate outline icon"></i> arxiv.org </button> </a>

Bootstrap - Inspired Techniques in Computation Intelligence

Robi Polikar
<span title="">2007</span> <i title="Institute of Electrical and Electronics Engineers (IEEE)"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/txj4mzfpbne4hhwgo2ku5cycbq" style="color: black;">IEEE Signal Processing Magazine</a> </i> &nbsp;
Ensemble of classifiers for incremental learning, data fusion, and missing feature analysis ] T his article is about the success story of a seemingly simple yet extremely powerful approach that has recently  ...  reached a celebrity status in statistical and engineering sciences.  ...  ACKNOWLEDGMENTS The work on ensemble-based incremental learning, data fusion, and missing feature algorithm development is supported by National Science Foundation under Grant No. ECS-0239090.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1109/msp.2007.4286565">doi:10.1109/msp.2007.4286565</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/4darrsqjcfgx7hzqefimqxcw2q">fatcat:4darrsqjcfgx7hzqefimqxcw2q</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20130718040405/http://users.rowan.edu:80/~polikar/RESEARCH/PUBLICATIONS/spm2007.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/cc/2d/cc2d54ea274b8458c981e268648f00f891824d8b.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1109/msp.2007.4286565"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="external alternate icon"></i> ieee.com </button> </a>

Variance Optimized Bagging [chapter]

Philip Derbeko, Ran El-Yaniv, Ron Meir
<span title="">2002</span> <i title="Springer Berlin Heidelberg"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/2w3awgokqne6te4nvlofavy5a4" style="color: black;">Lecture Notes in Computer Science</a> </i> &nbsp;
We test the new method on a number of binary classification problems from the UCI repository using a Support Vector Machine (SVM) as the base-classifier learning algorithm.  ...  We propose and study a new technique for aggregating an ensemble of bootstrapped classifiers.  ...  the better (high accuracy) classifiers also have smaller variance. 4 In Figure 4 we see the final 10-fold cross-validation average accuracy of Vogging and Bagging on the Voting dataset.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1007/3-540-36755-1_6">doi:10.1007/3-540-36755-1_6</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/sjh3ghmag5gfhf7bkgl6bjjhmy">fatcat:sjh3ghmag5gfhf7bkgl6bjjhmy</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20181030081838/https://link.springer.com/content/pdf/10.1007%2F3-540-36755-1_6.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/e1/22/e122e3a9bcd152d4329a7c8341ac173670064d1b.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1007/3-540-36755-1_6"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="external alternate icon"></i> springer.com </button> </a>

Granular computing-based approach for classification towards reduction of bias in ensemble learning

Han Liu, Mihaela Cocea
<span title="2016-11-11">2016</span> <i title="Springer Nature"> Granular Computing </i> &nbsp;
This paper contributes to the theoretical and empirical analysis of causes of bias in voting, towards advancing ensemble learning approaches through the use of probabilistic voting.  ...  Popular approaches of ensemble learning include Bagging and Boosting, which involve voting towards the final classification.  ...  , and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1007/s41066-016-0034-1">doi:10.1007/s41066-016-0034-1</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/4dc22sjcnrcxbnvzfyq7h6jllm">fatcat:4dc22sjcnrcxbnvzfyq7h6jllm</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20181030094746/https://link.springer.com/content/pdf/10.1007%2Fs41066-016-0034-1.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/dc/1f/dc1f733d94de9dabee907600b5e1c183676d1b89.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1007/s41066-016-0034-1"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="external alternate icon"></i> springer.com </button> </a>

Unsupervised encoding selection through ensemble pruning for biomedical classification [article]

Sebastian Spaenig, Alexander Michel, Dominik Heider
<span title="2022-02-09">2022</span> <i title="Cold Spring Harbor Laboratory"> bioRxiv </i> &nbsp; <span class="release-stage" >pre-print</span>
A crucial part is thereby the costly identification and validation.  ...  The workflow conducts multiple pruning methods to evaluate ensemble classifiers composed from a wide range of peptide encodings and base models.  ...  Note that for the computation of the CD, we concatenated the MCCs of all cross-validation runs, e.g., 12 * 100 MCCs for pfront, and 6 * 100 MCCs for bayes voting soft.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1101/2022.02.06.479282">doi:10.1101/2022.02.06.479282</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/42sodqw2dbdmfcqunp2caer6p4">fatcat:42sodqw2dbdmfcqunp2caer6p4</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20220211023810/https://www.biorxiv.org/content/biorxiv/early/2022/02/09/2022.02.06.479282.full.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/3a/a7/3aa78188cfa9fca2d9cfc7932fcf220224ecf241.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1101/2022.02.06.479282"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="external alternate icon"></i> biorxiv.org </button> </a>

Lessons in disguise: multivariate predictive mistakes in collective choice models

Bruce A. Desmarais
<span title="2011-01-21">2011</span> <i title="Springer Nature"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/x4rtmbpo4zdmxcmmih7fejvxh4" style="color: black;">Public Choice</a> </i> &nbsp;
Examples of this in domestic politics include roll-call voting in legislatures and decisions issued by multi-member courts of appeal.  ...  I demonstrate the use of JPEs on data from two published articles-one on U.S. Supreme Court voting and another on international defense alliances.  ...  Two critical choices in cross-validation are (1) the method used to split the sample and (2) the fit measure that is cross-validated.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1007/s11127-011-9767-1">doi:10.1007/s11127-011-9767-1</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/jz7bhkhxnveybaj4wj7z6co6pe">fatcat:jz7bhkhxnveybaj4wj7z6co6pe</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20150314204640/http://people.umass.edu:80/bruced/pubs/Desmarais_PC2012.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/be/92/be925c6b38bdd22cd163e8d7b1d52d8cc7ac683b.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1007/s11127-011-9767-1"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="external alternate icon"></i> springer.com </button> </a>

A comprehensive evaluation of multicategory classification methods for microarray gene expression cancer diagnosis

A. Statnikov, C. F. Aliferis, I. Tsamardinos, D. Hardin, S. Levy
<span title="2004-09-16">2004</span> <i title="Oxford University Press (OUP)"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/wmo54ba2jnemdingjj4fl3736a" style="color: black;">Bioinformatics</a> </i> &nbsp;
In order to equip the system with the optimum combination of classifier, gene selection and cross-validation methods, we performed a systematic and comprehensive evaluation of several major algorithms  ...  datasets with varying sample size, number of genes, and cancer types; (c) whether it is possible to increase diagnostic performance further using meta-learning in the form of ensemble classification;  ...  In particular, we used feedforward NN with one hidden layer and the number of units chosen from the set {2,5,10,30,50} based on cross-validation error.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1093/bioinformatics/bti033">doi:10.1093/bioinformatics/bti033</a> <a target="_blank" rel="external noopener" href="https://www.ncbi.nlm.nih.gov/pubmed/15374862">pmid:15374862</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/gr7vdtx2xjgnfjgfnzrqvstg4i">fatcat:gr7vdtx2xjgnfjgfnzrqvstg4i</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20070315125732/http://www.math.vanderbilt.edu:80/~hardin/papers/SATHL2004.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/a8/24/a8244689434f4dcedb7709e4441f5f7433846ce8.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1093/bioinformatics/bti033"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="external alternate icon"></i> oup.com </button> </a>

Domain adaptation of weighted majority votes via perturbed variation-based self-labeling [article]

Emilie Morvant
<span title="2014-10-01">2014</span> <i > arXiv </i> &nbsp; <span class="release-stage" >pre-print</span>
The core of this measure is a theoretical bound--the C-bound (Lacasse et al., 2007)--which involves the disagreement and leads to a well performing majority vote learning algorithm in usual non-adaptative  ...  Justified by a theoretical bound on the target risk of the vote, we provide to MinCq a target sample labeled thanks to a perturbed variation-based self-labeling focused on the regions where the source  ...  Each parameter is selected with a grid search via a classical 5-folds cross-validation for SVM, MinCq and TSVM, a reverse 5-folds cross-validation for DASVM, DASF, PBDA and NN-MinCq, and the PV-based validation  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/1410.0334v1">arXiv:1410.0334v1</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/ps75jotsdnejxexg6mgjsen4pe">fatcat:ps75jotsdnejxexg6mgjsen4pe</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20191018191649/https://arxiv.org/pdf/1410.0334v1.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/73/87/73878fb61b02fc317f6dc744a2b3437031ff72c1.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/1410.0334v1" title="arxiv.org access"> <button class="ui compact blue labeled icon button serp-button"> <i class="file alternate outline icon"></i> arxiv.org </button> </a>

Domain adaptation of weighted majority votes via perturbed variation-based self-labeling

Emilie Morvant
<span title="">2015</span> <i title="Elsevier BV"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/6r4znskbk5h2ngu345slqsm6eu" style="color: black;">Pattern Recognition Letters</a> </i> &nbsp;
In non-DA supervised setting, a theoretical bound - the C-bound - involves this disagreement and leads to a majority vote learning algorithm: MinCq.  ...  This arrives when one desires to learn, from a source distribution, a good weighted majority vote (over a set of classifiers) on a different target distribution.  ...  Each parameter is selected with a grid search via a classical 5-folds cross-validation for SVM, MinCq and TSVM, a reverse 5-folds cross-validation for DASVM, DASF, PBDA and NN-MinCq, and the PV-based validation  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1016/j.patrec.2014.08.013">doi:10.1016/j.patrec.2014.08.013</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/2uutvv2xofhm3jkapdk7g7pyre">fatcat:2uutvv2xofhm3jkapdk7g7pyre</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20170928061226/https://hal.archives-ouvertes.fr/hal-01056599/document" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/02/9d/029d1024df8025d13095dae33f5eb9ef74c820b1.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1016/j.patrec.2014.08.013"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="external alternate icon"></i> elsevier.com </button> </a>

Selecting Representative Data Sets [chapter]

Tomas Borovicka, Marcel Jirina, Pavel Kordik, Marcel Jiri
<span title="2012-09-12">2012</span> <i title="InTech"> Advances in Data Mining Knowledge Discovery and Applications </i> &nbsp;
Acknowledgements This work was supported by the Institute of Computer Science of the Czech Academy of Sciences RVO: 67985807. LG 12020.  ...  The most known and most popular in machine learning is the paired t test and its improved version the k-fold cross-validated pair test.  ...  Several base models are learned on the training set and then applied to the validation set.  ... 
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Morphological classifiers

É.O. Rodrigues, A. Conci, P. Liatsis
<span title="">2018</span> <i title="Elsevier BV"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/jm6w2xclfzguxnhmnmq5omebpi" style="color: black;">Pattern Recognition</a> </i> &nbsp;
This work proposes a new type of classifier called Morphological Classifier (MC). MCs aggregate concepts from mathematical morphology and supervised learning.  ...  MCs are fundamentally based on set theory, and their classification model can be a mathematical set itself.  ...  Introduction Supervised learning consists of analysing labelled instances in order to generate a classification model capable of producing labels for unlabelled instances.  ... 
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