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1
Performance analysis of support vector machines classifiers in breast cancer mammography recognition
Performance analysis of support vector machines classifiers in breast cancer mammography recognition
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Performance analysis of support vector machines classifiers in breast cancer mammography recognition

Neural computing & applications, 2014-04, Vol.24 (5), p.1163-1177 [Peer Reviewed Journal]

Springer-Verlag London 2013 ;2015 INIST-CNRS ;ISSN: 0941-0643 ;EISSN: 1433-3058 ;DOI: 10.1007/s00521-012-1324-4

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2
COVID-19 cough classification using machine learning and global smartphone recordings
COVID-19 cough classification using machine learning and global smartphone recordings
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COVID-19 cough classification using machine learning and global smartphone recordings

Computers in biology and medicine, 2021-08, Vol.135, p.104572-104572, Article 104572 [Peer Reviewed Journal]

2021 Elsevier Ltd ;Copyright © 2021 Elsevier Ltd. All rights reserved. ;2021. Elsevier Ltd ;2021 Elsevier Ltd. All rights reserved. 2021 Elsevier Ltd ;ISSN: 0010-4825 ;EISSN: 1879-0534 ;DOI: 10.1016/j.compbiomed.2021.104572 ;PMID: 34182331

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3
Machine Learning methods for solar radiation forecasting: a review
Machine Learning methods for solar radiation forecasting: a review
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Machine Learning methods for solar radiation forecasting: a review

Renewable energy, 2017-05, Vol.105, p.569-582 [Peer Reviewed Journal]

Distributed under a Creative Commons Attribution 4.0 International License ;ISSN: 0960-1481 ;EISSN: 1879-0682 ;DOI: 10.1016/j.renene.2016.12.095

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4
Comparison of Random Forest, k-Nearest Neighbor, and Support Vector Machine Classifiers for Land Cover Classification Using Sentinel-2 Imagery
Comparison of Random Forest, k-Nearest Neighbor, and Support Vector Machine Classifiers for Land Cover Classification Using Sentinel-2 Imagery
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Comparison of Random Forest, k-Nearest Neighbor, and Support Vector Machine Classifiers for Land Cover Classification Using Sentinel-2 Imagery

Sensors (Basel, Switzerland), 2017-12, Vol.18 (1), p.18 [Peer Reviewed Journal]

2018. This work is licensed under https://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. ;2017 by the authors. 2017 ;ISSN: 1424-8220 ;EISSN: 1424-8220 ;DOI: 10.3390/s18010018 ;PMID: 29271909

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5
SVM and MRF-Based Method for Accurate Classification of Hyperspectral Images
SVM and MRF-Based Method for Accurate Classification of Hyperspectral Images
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SVM and MRF-Based Method for Accurate Classification of Hyperspectral Images

IEEE geoscience and remote sensing letters, 2010-10, Vol.7 (4), p.736-740 [Peer Reviewed Journal]

Distributed under a Creative Commons Attribution 4.0 International License ;ISSN: 1545-598X ;EISSN: 1558-0571 ;DOI: 10.1109/LGRS.2010.2047711

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6
Comparing different supervised machine learning algorithms for disease prediction
Comparing different supervised machine learning algorithms for disease prediction
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Comparing different supervised machine learning algorithms for disease prediction

BMC medical informatics and decision making, 2019-12, Vol.19 (1), p.281-281, Article 281 [Peer Reviewed Journal]

COPYRIGHT 2019 BioMed Central Ltd. ;2019. This work is licensed under http://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. ;The Author(s). 2019 ;ISSN: 1472-6947 ;EISSN: 1472-6947 ;DOI: 10.1186/s12911-019-1004-8 ;PMID: 31864346

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7
Selecting training sets for support vector machines: a review
Selecting training sets for support vector machines: a review
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Selecting training sets for support vector machines: a review

The Artificial intelligence review, 2019-08, Vol.52 (2), p.857-900 [Peer Reviewed Journal]

The Author(s) 2018 ;COPYRIGHT 2019 Springer ;Artificial Intelligence Review is a copyright of Springer, (2018). All Rights Reserved. © 2018. This work is published under http://creativecommons.org/licenses/by/4.0 (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. ;ISSN: 0269-2821 ;EISSN: 1573-7462 ;DOI: 10.1007/s10462-017-9611-1

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8
Exploiting machine learning for end-to-end drug discovery and development
Exploiting machine learning for end-to-end drug discovery and development
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Exploiting machine learning for end-to-end drug discovery and development

Nature materials, 2019-05, Vol.18 (5), p.435-441 [Peer Reviewed Journal]

Springer Nature Limited 2019. ;ISSN: 1476-1122 ;EISSN: 1476-4660 ;DOI: 10.1038/s41563-019-0338-z ;PMID: 31000803

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9
Support vector machines based non-contact fault diagnosis system for bearings
Support vector machines based non-contact fault diagnosis system for bearings
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Support vector machines based non-contact fault diagnosis system for bearings

Journal of intelligent manufacturing, 2020-06, Vol.31 (5), p.1275-1289 [Peer Reviewed Journal]

Springer Science+Business Media, LLC, part of Springer Nature 2019 ;Springer Science+Business Media, LLC, part of Springer Nature 2019. ;ISSN: 0956-5515 ;EISSN: 1572-8145 ;DOI: 10.1007/s10845-019-01511-x

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10
Using machine learning to predict student difficulties from learning session data
Using machine learning to predict student difficulties from learning session data
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Using machine learning to predict student difficulties from learning session data

The Artificial intelligence review, 2019-06, Vol.52 (1), p.381-407 [Peer Reviewed Journal]

Springer Science+Business Media B.V., part of Springer Nature 2018 ;COPYRIGHT 2019 Springer ;Artificial Intelligence Review is a copyright of Springer, (2018). All Rights Reserved. ;ISSN: 0269-2821 ;EISSN: 1573-7462 ;DOI: 10.1007/s10462-018-9620-8

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11
Support vector machine for modelling and simulation of heat exchangers
Support vector machine for modelling and simulation of heat exchangers
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Support vector machine for modelling and simulation of heat exchangers

Thermal science, 2020, Vol.24 (1 Part B), p.499-503 [Peer Reviewed Journal]

2020. This work is licensed under https://creativecommons.org/licenses/by-nc-nd/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. ;ISSN: 0354-9836 ;EISSN: 2334-7163 ;DOI: 10.2298/TSCI190419398M

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12
Comparative evaluation of machine learning models for groundwater quality assessment
Comparative evaluation of machine learning models for groundwater quality assessment
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Comparative evaluation of machine learning models for groundwater quality assessment

Environmental monitoring and assessment, 2020-12, Vol.192 (12), p.776, Article 776 [Peer Reviewed Journal]

Springer Nature Switzerland AG 2020 ;Springer Nature Switzerland AG 2020. ;ISSN: 0167-6369 ;EISSN: 1573-2959 ;DOI: 10.1007/s10661-020-08695-3 ;PMID: 33219864

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13
Applications of Support Vector Machine (SVM) Learning in Cancer Genomics
Applications of Support Vector Machine (SVM) Learning in Cancer Genomics
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Applications of Support Vector Machine (SVM) Learning in Cancer Genomics

Cancer genomics & proteomics, 2018-01, Vol.15 (1), p.41-51 [Peer Reviewed Journal]

Copyright 2018, International Institute of Anticancer Research 2018 ;ISSN: 1109-6535 ;EISSN: 1790-6245 ;DOI: 10.21873/cgp.20063 ;PMID: 29275361

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14
A review on multi-class TWSVM
A review on multi-class TWSVM
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A review on multi-class TWSVM

The Artificial intelligence review, 2019-08, Vol.52 (2), p.775-801 [Peer Reviewed Journal]

Springer Science+Business Media B.V. 2017 ;COPYRIGHT 2019 Springer ;Artificial Intelligence Review is a copyright of Springer, (2017). All Rights Reserved. ;ISSN: 0269-2821 ;EISSN: 1573-7462 ;DOI: 10.1007/s10462-017-9586-y

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15
Twin Support Vector Machine: A review from 2007 to 2014
Twin Support Vector Machine: A review from 2007 to 2014
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Twin Support Vector Machine: A review from 2007 to 2014

Egyptian informatics journal, 2015-03, Vol.16 (1), p.55-69 [Peer Reviewed Journal]

2015 ;ISSN: 1110-8665 ;EISSN: 2090-4754 ;DOI: 10.1016/j.eij.2014.12.003

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16
COVID-19 detection using deep learning models to exploit Social Mimic Optimization and structured chest X-ray images using fuzzy color and stacking approaches
COVID-19 detection using deep learning models to exploit Social Mimic Optimization and structured chest X-ray images using fuzzy color and stacking approaches
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COVID-19 detection using deep learning models to exploit Social Mimic Optimization and structured chest X-ray images using fuzzy color and stacking approaches

Computers in biology and medicine, 2020-06, Vol.121, p.103805-103805, Article 103805 [Peer Reviewed Journal]

2020 Elsevier Ltd ;Copyright © 2020 Elsevier Ltd. All rights reserved. ;2020. Elsevier Ltd ;2020 Elsevier Ltd. All rights reserved. 2020 Elsevier Ltd ;ISSN: 0010-4825 ;EISSN: 1879-0534 ;DOI: 10.1016/j.compbiomed.2020.103805 ;PMID: 32568679

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17
Support Vector Machine Versus Random Forest for Remote Sensing Image Classification: A Meta-Analysis and Systematic Review
Support Vector Machine Versus Random Forest for Remote Sensing Image Classification: A Meta-Analysis and Systematic Review
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Support Vector Machine Versus Random Forest for Remote Sensing Image Classification: A Meta-Analysis and Systematic Review

IEEE journal of selected topics in applied earth observations and remote sensing, 2020, Vol.13, p.6308-6325 [Peer Reviewed Journal]

Copyright The Institute of Electrical and Electronics Engineers, Inc. (IEEE) 2020 ;ISSN: 1939-1404 ;EISSN: 2151-1535 ;DOI: 10.1109/JSTARS.2020.3026724 ;CODEN: IJSTHZ

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18
Prediction of Alzheimer's disease and mild cognitive impairment using cortical morphological patterns
Prediction of Alzheimer's disease and mild cognitive impairment using cortical morphological patterns
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Prediction of Alzheimer's disease and mild cognitive impairment using cortical morphological patterns

Human brain mapping, 2013-12, Vol.34 (12), p.3411-3425 [Peer Reviewed Journal]

Copyright © 2012 Wiley Periodicals, Inc. ;2014 INIST-CNRS ;ISSN: 1065-9471 ;EISSN: 1097-0193 ;DOI: 10.1002/hbm.22156 ;PMID: 22927119

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19
Urban Tree Species Classification Using a WorldView-2/3 and LiDAR Data Fusion Approach and Deep Learning
Urban Tree Species Classification Using a WorldView-2/3 and LiDAR Data Fusion Approach and Deep Learning
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Urban Tree Species Classification Using a WorldView-2/3 and LiDAR Data Fusion Approach and Deep Learning

Sensors (Basel, Switzerland), 2019-03, Vol.19 (6), p.1284 [Peer Reviewed Journal]

2019 by the authors. 2019 ;ISSN: 1424-8220 ;EISSN: 1424-8220 ;DOI: 10.3390/s19061284 ;PMID: 30875732

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20
SVM-RFE: selection and visualization of the most relevant features through non-linear kernels
SVM-RFE: selection and visualization of the most relevant features through non-linear kernels
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SVM-RFE: selection and visualization of the most relevant features through non-linear kernels

BMC bioinformatics, 2018-11, Vol.19 (1), p.432-432, Article 432 [Peer Reviewed Journal]

COPYRIGHT 2018 BioMed Central Ltd. ;cc-by (c) Sanz, Hector et al., 2018 info:eu-repo/semantics/openAccess http://creativecommons.org/licenses/by/3.0/es ;The Author(s). 2018 ;ISSN: 1471-2105 ;EISSN: 1471-2105 ;DOI: 10.1186/s12859-018-2451-4 ;PMID: 30453885

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