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Proposal for a Unified Methodology for Evaluating Supervised and Non-supervised Classification Algorithms [chapter]

Salvador Godoy-Calderón, J. Fco. Martínez-Trinidad, Manuel Lazo Cortés
<span title="">2006</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;
There is presently no unified methodology that allows the evaluation of supervised and non-supervised classification algorithms.  ...  This paper proposes a unified methodology to evaluate classification problems of both kinds, that offers the possibility of making comparative evaluations and yields a larger amount of information to the  ...  Thanks to the definitions previously established, such comparison is a common element between supervised and non-supervised problems and unifies the evaluation methodology.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1007/11892755_70">doi:10.1007/11892755_70</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/cd4ws5tmwrcqlfr35v4jg4txqu">fatcat:cd4ws5tmwrcqlfr35v4jg4txqu</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20190505115617/https://link.springer.com/content/pdf/10.1007%2F11892755_70.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/c6/c1/c6c1bf964e9dece96dd443f63b9460b2137f6e7f.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1007/11892755_70"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="external alternate icon"></i> springer.com </button> </a>

General Supervision via Probabilistic Transformations [article]

Santiago Mazuelas, Aritz Perez
<span title="2019-01-24">2019</span> <i > arXiv </i> &nbsp; <span class="release-stage" >pre-print</span>
This paper presents a unifying framework for supervised classification with general ensembles of training data, and proposes the learning methodology of generalized robust risk minimization (GRRM).  ...  Different types of training data have led to numerous schemes for supervised classification.  ...  The introduced methodology of generalized robust risk minimization (GRRM) can enable to develop learning algorithms for current and novel supervision schemes in a unified manner.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/1901.08552v1">arXiv:1901.08552v1</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/xafqsvg54zeu5i4bnvhuwzajpe">fatcat:xafqsvg54zeu5i4bnvhuwzajpe</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20191013140956/https://arxiv.org/pdf/1901.08552v1.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/0e/ac/0eac528e6521f8d2a44f66f8dfaf0b0f5eb21220.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/1901.08552v1" 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>

Automatic Generation Of Training Data For Hyperspectral Image Classification Using Support Vector Machine

B. Abbasi, H. Arefi, B. Bigdeli, S. Roessner
<span title="2015-04-29">2015</span> <i title="Copernicus GmbH"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/i74shj7anreaxjo327fokng66m" style="color: black;">The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences</a> </i> &nbsp;
To improve classification accuracy, the hyperspectral image and 3K DSM were utilized simultaneously to perform image classification.  ...  Also, we created initial segment regarding to ground pixel after geodesic based filtering of DSM and elimination of the non-ground pixels.  ...  ACKNOWLEDGEMENTS We would like to thank Deutsches Zentrum fur Luft-und Raumfahr (DLR, German Aerospace Center) for providing us the Hyspex imagery and DSM data.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.5194/isprsarchives-xl-7-w3-575-2015">doi:10.5194/isprsarchives-xl-7-w3-575-2015</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/psyv7cszzffzrgwvegve23u3wi">fatcat:psyv7cszzffzrgwvegve23u3wi</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20170706091854/http://www.int-arch-photogramm-remote-sens-spatial-inf-sci.net/XL-7-W3/575/2015/isprsarchives-XL-7-W3-575-2015.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/43/ae/43ae53b0e6e4fc2f8568e7bda7344a059a11b273.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.5194/isprsarchives-xl-7-w3-575-2015"> <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>

Information Theoretic Evaluation of Privacy-Leakage, Interpretability, and Transferability for Trustworthy AI [article]

Mohit Kumar, Bernhard A. Moser, Lukas Fischer, Bernhard Freudenthaler
<span title="2022-04-12">2022</span> <i > arXiv </i> &nbsp; <span class="release-stage" >pre-print</span>
A unified approach to "privacy-preserving interpretable and transferable learning" is considered for studying and optimizing the tradeoffs between privacy, interpretability, and transferability aspects  ...  The study presents a unified information theoretic approach to study different aspects of trustworthy AI in a rigorous analytical manner.  ...  AI for Healthcare Systems); the Federal Ministry for Climate Action, Environment, Energy, Mobility, Innovation and Technology (BMK); the Federal Ministry for Digital and Economic Affairs (BMDW); and the  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/2106.06046v5">arXiv:2106.06046v5</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/lm4irfkervgilds6ly65c624vq">fatcat:lm4irfkervgilds6ly65c624vq</a> </span>
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An outlook: machine learning in hyperspectral image classification and dimensionality reduction techniques

Tatireddy Reddy, Jonnadula Harikiran
<span title="2022-01-07">2022</span> <i title="IM Publications Open LLP"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/vd3srshgk5cszja7pj642mcc2y" style="color: black;">Journal of Spectral Imaging</a> </i> &nbsp;
As a result, this paper reviews three different types of hyperspectral image machine learning classification methods: cluster analysis, supervised and semi-supervised classification.  ...  Furthermore, this review will assist as a reference for future research to improve the classification and dimensionality reduction approaches.  ...  From this, it can be observed that most of the recent hyperspectral image classification algorithms are based on supervised and semi-supervised approaches.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1255/jsi.2022.a1">doi:10.1255/jsi.2022.a1</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/rue5klkmlfcrzftepc6lzfcbfe">fatcat:rue5klkmlfcrzftepc6lzfcbfe</a> </span>
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Automated Machine Learning – a brief review at the end of the early years [article]

Hugo Jair Escalante
<span title="2020-08-24">2020</span> <i > arXiv </i> &nbsp; <span class="release-stage" >pre-print</span>
More specifically, in this chapter an introduction to AutoML for supervised learning is provided and an historical review of progress in this field is presented.  ...  Automated machine learning (AutoML) is the sub-field of machine learning that aims at automating, to some extend, all stages of the design of a machine learning system.  ...  Spam filtering methods, face recognition systems, handwritten character recognition techniques and text classification methodologies are only a few of the classical applications relying in supervised learning  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/2008.08516v3">arXiv:2008.08516v3</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/xygxkejxvvd5joz2nouxya5ska">fatcat:xygxkejxvvd5joz2nouxya5ska</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20200906195106/https://arxiv.org/pdf/2008.08516v3.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/2008.08516v3" 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>

Mining Histopathological Images via Composite Hashing and Online Learning [chapter]

Xiaofan Zhang, Lin Yang, Wei Liu, Hai Su, Shaoting Zhang
<span title="">2014</span> <i title="Springer International Publishing"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/2w3awgokqne6te4nvlofavy5a4" style="color: black;">Lecture Notes in Computer Science</a> </i> &nbsp;
Specifically, we propose a principled framework to unify hashing-based image retrieval and supervised learning.  ...  Upon a local data subset that retains the retrieved images, supervised learning methods are applied on-the-fly to model image structures for accurate classification.  ...  The main contribution lies in unifying scalable CBIR based on hashing and supervised learning methods on-the-fly, which turns out to exhibit high classification accuracy and computational efficiency.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1007/978-3-319-10470-6_60">doi:10.1007/978-3-319-10470-6_60</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/iadlmt7fyvd2rg2actcwa7wxpa">fatcat:iadlmt7fyvd2rg2actcwa7wxpa</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20170809042716/http://www.ee.columbia.edu/~wliu/MICCAI14_composite_hash.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/c4/37/c437d0485217685f9ea42c33e492090b58de1db6.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1007/978-3-319-10470-6_60"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="external alternate icon"></i> springer.com </button> </a>

SemiFed: Semi-supervised Federated Learning with Consistency and Pseudo-Labeling [article]

Haowen Lin, Jian Lou, Li Xiong, Cyrus Shahabi
<span title="2021-08-21">2021</span> <i > arXiv </i> &nbsp; <span class="release-stage" >pre-print</span>
We propose a new framework dubbed SemiFed that unifies two dominant approaches for semi-supervised learning: consistency regularization and pseudo-labeling.  ...  We borrow ideas from semi-supervised learning methods where a large amount of unlabeled data is utilized to improve the model's accuracy despite limited access to labeled examples.  ...  We propose Semi-Supervised Federated Learning (SemiFed) as a unified framework and apply it to image classification with limited labeled samples.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/2108.09412v1">arXiv:2108.09412v1</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/4teau6zlubbzpcpgpx5bobmu2u">fatcat:4teau6zlubbzpcpgpx5bobmu2u</a> </span>
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Retinal Image Classification by Self-supervised Fuzzy Clustering Network

Yueguo Luo, Jing Pan, Shaoshuai Fan, Zeyu Du, Guanghua Zhang
<span title="">2020</span> <i title="Institute of Electrical and Electronics Engineers (IEEE)"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/q7qi7j4ckfac7ehf3mjbso4hne" style="color: black;">IEEE Access</a> </i> &nbsp;
Specifically, we propose a Self-supervised Fuzzy Clustering Network (SFCN) by a feature learning module, reconstruction module, and a fuzzy self-supervision module.  ...  INDEX TERMS Retinal image classification, self-supervised, fuzzy clustering, unsupervised learning. This work is licensed under a Creative Commons Attribution 4.0 License.  ...  FURTHER ANALYSIS 1) EVALUATION OF THE FUZZY C-MEANS ALGORITHM To evaluate the effectiveness of using fuzzy C-means algorithm as the self-supervision guiding, we replace the fuzzy C-means algorithm by two  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1109/access.2020.2994047">doi:10.1109/access.2020.2994047</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/t77mpusgerb5tb2g7vuitxdseq">fatcat:t77mpusgerb5tb2g7vuitxdseq</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20210429153245/https://ieeexplore.ieee.org/ielx7/6287639/8948470/09091815.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/79/42/79424e2c0023fb76f84965591b216b869fcb1227.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1109/access.2020.2994047"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="unlock alternate icon" style="background-color: #fb971f;"></i> ieee.com </button> </a>

Supervised Non-negative Matrix Factorization Methods for MALDI Imaging Applications

Johannes Leuschner, Maximilian Schmidt, Pascal Fernsel, Delf Lachmund, Tobias Boskamp, Peter Maass
<span title="2018-11-03">2018</span> <i title="Oxford University Press"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/wmo54ba2jnemdingjj4fl3736a" style="color: black;">Bioinformatics</a> </i> &nbsp;
('KMU-innovativ: Medizintechnik' program, contract number 13GW0081) and the German Science Foundation within the framework of GRK 2224/1 2224 'p 3 : Parameter Identification-Analysis, Algorithms, Applications  ...  Kriegsmann (Institute of Pathology, Heidelberg University Hospital), Dr A. Warth (Thoracic Pathology, Heidelberg University Hospital), Prof. Dr H.  ...  Moreover, this approach allows to unify the feature extraction and classifier training steps, reducing computation time and algorithmic complexity.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1093/bioinformatics/bty909">doi:10.1093/bioinformatics/bty909</a> <a target="_blank" rel="external noopener" href="https://www.ncbi.nlm.nih.gov/pubmed/30395171">pmid:30395171</a> <a target="_blank" rel="external noopener" href="https://pubmed.ncbi.nlm.nih.gov/PMC6546133/">pmcid:PMC6546133</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/dkauhzyv4ngirmk7quiqh5kvpa">fatcat:dkauhzyv4ngirmk7quiqh5kvpa</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20190505070822/https://watermark.silverchair.com/bty909.pdf?token=AQECAHi208BE49Ooan9kkhW_Ercy7Dm3ZL_9Cf3qfKAc485ysgAAAngwggJ0BgkqhkiG9w0BBwagggJlMIICYQIBADCCAloGCSqGSIb3DQEHATAeBglghkgBZQMEAS4wEQQMtPm-Ug0e5bwPxpK5AgEQgIICK-D4wv7Lsq-diScNAaJto5vu8lDpzbv2qXEKFUAxj8Z8fPsTDBUJEj8rThH4G8zNexn7SxRYsYgHg9BDIozKEGn19l8t9EfejN7IQCfg6ShGZ2RIADTq7tBxTiXdp5K9aCDZUI0YGdbqnIvJTAvcGYXdYEFW-3_IsqmVia-9cXbRs-6mU0F1kT_5TEIf-auEcfstl2gDyNafgc0G6xI6dOaN_v5M2qlyB5OL15mPNQLpT7UK4udBOGP3BPE6qy1lGRQUS2jAtt3zREiMO_ZoajlmJtCG6QctXeA8oDlPP1-BFzanyvu-Qn1EN-jIX3-Om9Ob6z5HWMy9IPRkPqLFCKM9t3-EJxPHh0eevhCPtM-u_VN0dBsat4JysY0n0WOyagcxpJbzbDJaWXh2IiJkF2-lfo39LBPUc_cvbZ-Mlf1l_daeQw3oF-V6QhgMl280SHlt6LlGM9pETJZZCCR8QS9M2n87s7LuULk9uH6K0GmUoFCWQmqRH0LzC-57ev6-b7CWjD5uYxmC2tXeubbNq_pps7bXogdEoDYkLUUpUIoUTQUz9SExTwiO3qI_IjjrqX8kxYf2BVt7RFngxPaxVyjXKZ8GglQKQXu6_W0pYIDH0k0-_sXWtowhT2tRhuZApJKFuxAT-NJu7VqXj-g9df0DzEz_7sNKwZmVhM9MnrEmH-1Y50cUoNCF8tZnpGi9ijVUv4_jQQlnPGGgRzyhWT5ZMnz5ZmDS5Vk3LQ" 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/ca/89/ca89ad1061fc0be4e21bb71a2d982d8cec785dc3.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1093/bioinformatics/bty909"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="external alternate icon"></i> oup.com </button> </a> <a target="_blank" rel="external noopener" href="https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6546133" title="pubmed link"> <button class="ui compact blue labeled icon button serp-button"> <i class="file alternate outline icon"></i> pubmed.gov </button> </a>

Machine Learning Methods with Noisy, Incomplete or Small Datasets

Cesar F. Caiafa, Zhe Sun, Toshihisa Tanaka, Pere Marti-Puig, Jordi Solé-Casals
<span title="">2021</span> <i title="MDPI AG"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/smrngspzhzce7dy6ofycrfxbim" style="color: black;">Applied Sciences</a> </i> &nbsp;
Contributions in applied sciences include medical applications, epidemic management tools, methodological work, and industrial applications, among others.  ...  These papers provide a variety of novel approaches to real-world machine learning problems where available datasets suffer from imperfections such as missing values, noise or artefacts.  ...  (South Korea) developed feature extraction methods based on the non-negative matrix factorization (NMF) algorithm and it is applied in weakly supervised sound event detection.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.3390/app11094132">doi:10.3390/app11094132</a> <a target="_blank" rel="external noopener" href="https://doaj.org/article/b756026d4f1b45e89f158fe4378f7e8c">doaj:b756026d4f1b45e89f158fe4378f7e8c</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/zpqxuxf5ora2xk3zpmge73tyl4">fatcat:zpqxuxf5ora2xk3zpmge73tyl4</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20210615132451/https://res.mdpi.com/d_attachment/applsci/applsci-11-04132/article_deploy/applsci-11-04132-v2.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/17/34/1734963282f7c6768144e6bdb977850045ce8829.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.3390/app11094132"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="unlock alternate icon" style="background-color: #fb971f;"></i> mdpi.com </button> </a>

Billion-Scale Pretraining with Vision Transformers for Multi-Task Visual Representations [article]

Josh Beal, Hao-Yu Wu, Dong Huk Park, Andrew Zhai, Dmitry Kislyuk
<span title="2021-08-12">2021</span> <i > arXiv </i> &nbsp; <span class="release-stage" >pre-print</span>
Through a comprehensive study of offline and online evaluation, we show that large-scale Transformer-based pretraining provides significant benefits to industry computer vision applications.  ...  In this work, we describe how we (1) generate a dataset with over a billion images via large weakly-supervised pretraining to improve the performance of these visual representations, and (2) leverage Transformers  ...  The authors would like to thank Eric Tzeng, Raymond Shiau, Kofi Boakye, Vahid Kazemi, and Chuck Rosenberg for valuable discussions regarding the paper, and the anonymous reviewers and ACs for their helpful  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/2108.05887v1">arXiv:2108.05887v1</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/gm5lzf4pkrg3zez7unuq7epp3a">fatcat:gm5lzf4pkrg3zez7unuq7epp3a</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20210903122327/https://arxiv.org/pdf/2108.05887v1.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/c1/07/c1075fa775bb8c5de915dc9ab6dacc79706a9613.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/2108.05887v1" 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>

Unsupervised Anomaly Detection of Healthcare Providers Using Generative Adversarial Networks [chapter]

Krishnan Naidoo, Vukosi Marivate
<span title="">2020</span> <i title="Springer International Publishing"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/2w3awgokqne6te4nvlofavy5a4" style="color: black;">Lecture Notes in Computer Science</a> </i> &nbsp;
This study evaluates previous anomaly detection machine learning models and proposes an unsupervised framework to identify anomalies using a Generative Adversarial Network (GANs) model.  ...  With rising healthcare costs, healthcare fraud is a major contributor to these increasing healthcare costs.  ...  Methodology Data Collection and Pre-processing To evaluate our anomaly detection approach, we describe the datasets and preprocessing in detail.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1007/978-3-030-44999-5_35">doi:10.1007/978-3-030-44999-5_35</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/qxuczgunonaazh7a6ei6saifze">fatcat:qxuczgunonaazh7a6ei6saifze</a> </span>
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Coastline Change Modelling Induced by Climate Change Using Geospatial Techniques in Togo (West Africa)

Yawo Konko, Appollonia Okhimambe, Pouwèréou Nimon, Jerry Asaana, Jean Paul Rudant, Kouami Kokou
<span title="">2020</span> <i title="Scientific Research Publishing, Inc."> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/56mt7utebzfrbikxjna5tmotb4" style="color: black;">Advances in Remote Sensing</a> </i> &nbsp;
The results revealed that the SVM Supervised Classification method showed good performance on linear and non-linear coastal surface than the other methods.  ...  It assesses the performance of Otsu threshold segmentation, Iso Cluster Unsupervised Classification and Support Vector Machine (SVM) Supervised Classification methods for the extraction of the shoreline  ...  Bawinabadi Bagaram from Spatial Optimization Lab, School of Environmental and Forest Sciences, University of Washington for his contribution.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.4236/ars.2020.92005">doi:10.4236/ars.2020.92005</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/yic242snhfgmnp4svcfjaougai">fatcat:yic242snhfgmnp4svcfjaougai</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20211202121059/https://www.scirp.org/pdf/ars_2020060515200563.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/c0/09/c009144b7cf4280451dc75645a756d38770c4397.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.4236/ars.2020.92005"> <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>

Data-Driven Based Approach to Aid Parkinson's Disease Diagnosis

Nicolas Khoury, Ferhat Attal, Yacine Amirat, Latifa Oukhellou, Samer Mohammed
<span title="2019-01-10">2019</span> <i title="MDPI AG"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/taedaf6aozg7vitz5dpgkojane" style="color: black;">Sensors</a> </i> &nbsp;
A classification engine assigns subjects to healthy or Parkinsonian classes.  ...  The proposed methodology uses both supervised classification methods including K-nearest neighbour (K-NN), decision tree (DT), random forest (RF), Naïve Bayes (NB), support vector machine (SVM) and unsupervised  ...  It is a non-parametric supervised classification method. In K-NN, no explicit or modelling phase occurs before the classification phase.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.3390/s19020242">doi:10.3390/s19020242</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/d3hexzqsuraehnjjat7y4fyije">fatcat:d3hexzqsuraehnjjat7y4fyije</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20190430213809/https://res.mdpi.com/sensors/sensors-19-00242/article_deploy/sensors-19-00242-v2.pdf?filename=&amp;attachment=1" 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/f6/52/f652aabf3c557429933d01873ec8ab0d2eab8acf.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.3390/s19020242"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="unlock alternate icon" style="background-color: #fb971f;"></i> mdpi.com </button> </a>
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