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1
100% classification accuracy considered harmful: the normalized information transfer factor explains the accuracy paradox
100% classification accuracy considered harmful: the normalized information transfer factor explains the accuracy paradox
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100% classification accuracy considered harmful: the normalized information transfer factor explains the accuracy paradox

PloS one, 2014-01, Vol.9 (1), p.e84217-e84217 [Peer Reviewed Journal]

COPYRIGHT 2014 Public Library of Science ;COPYRIGHT 2014 Public Library of Science ;2014 Valverde-Albacete, Peláez-Moreno. This is an open-access article distributed under the terms of the Creative Commons Attribution License: http://creativecommons.org/licenses/by/4.0/ (the “License”), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. ;2014 Valverde-Albacete, Peláez-Moreno 2014 Valverde-Albacete, Peláez-Moreno ;ISSN: 1932-6203 ;EISSN: 1932-6203 ;DOI: 10.1371/journal.pone.0084217 ;PMID: 24427282

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2
11K Hands: Gender recognition and biometric identification using a large dataset of hand images
11K Hands: Gender recognition and biometric identification using a large dataset of hand images
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11K Hands: Gender recognition and biometric identification using a large dataset of hand images

Multimedia tools and applications, 2019-08, Vol.78 (15), p.20835-20854 [Peer Reviewed Journal]

Springer Science+Business Media, LLC, part of Springer Nature 2019 ;Multimedia Tools and Applications is a copyright of Springer, (2019). All Rights Reserved. ;ISSN: 1380-7501 ;EISSN: 1573-7721 ;DOI: 10.1007/s11042-019-7424-8

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3
1D Convolutional Neural Networks Versus Automatic Classifiers for Known LPI Radar Signals Under White Gaussian Noise
1D Convolutional Neural Networks Versus Automatic Classifiers for Known LPI Radar Signals Under White Gaussian Noise
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1D Convolutional Neural Networks Versus Automatic Classifiers for Known LPI Radar Signals Under White Gaussian Noise

IEEE access, 2020, Vol.8, p.180534-180543 [Peer Reviewed Journal]

Copyright The Institute of Electrical and Electronics Engineers, Inc. (IEEE) 2020 ;ISSN: 2169-3536 ;EISSN: 2169-3536 ;DOI: 10.1109/ACCESS.2020.3027472 ;CODEN: IAECCG

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4
2-D shape recognition using recursive landmark determination and fuzzy ART network learning
2-D shape recognition using recursive landmark determination and fuzzy ART network learning
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2-D shape recognition using recursive landmark determination and fuzzy ART network learning

Neural processing letters, 2003-10, Vol.18 (2), p.81-95 [Peer Reviewed Journal]

2004 INIST-CNRS ;Kluwer Academic Publishers 2003. ;ISSN: 1370-4621 ;EISSN: 1573-773X ;DOI: 10.1023/A:1026261202044

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5
2-HDCNN: A two-tier hybrid dual convolution neural network feature fusion approach for diagnosing malignant melanoma
2-HDCNN: A two-tier hybrid dual convolution neural network feature fusion approach for diagnosing malignant melanoma
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2-HDCNN: A two-tier hybrid dual convolution neural network feature fusion approach for diagnosing malignant melanoma

Computers in biology and medicine, 2023-01, Vol.152, p.106333-106333, Article 106333 [Peer Reviewed Journal]

2022 ;Copyright © 2022. Published by Elsevier Ltd. ;Copyright Elsevier Limited Jan 2023 ;ISSN: 0010-4825 ;EISSN: 1879-0534 ;DOI: 10.1016/j.compbiomed.2022.106333 ;PMID: 36463793

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6
2D compressed learning: support matrix machine with bilinear random projections
2D compressed learning: support matrix machine with bilinear random projections
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2D compressed learning: support matrix machine with bilinear random projections

Machine learning, 2019-12, Vol.108 (12), p.2035-2060 [Peer Reviewed Journal]

The Author(s), under exclusive licence to Springer Science+Business Media LLC, part of Springer Nature 2019 ;Machine Learning is a copyright of Springer, (2019). All Rights Reserved. ;ISSN: 0885-6125 ;EISSN: 1573-0565 ;DOI: 10.1007/s10994-019-05804-3

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7
2DPCANet: a deep leaning network for face recognition
2DPCANet: a deep leaning network for face recognition
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2DPCANet: a deep leaning network for face recognition

Multimedia tools and applications, 2018-05, Vol.77 (10), p.12919-12934 [Peer Reviewed Journal]

Springer Science+Business Media, LLC 2017 ;Multimedia Tools and Applications is a copyright of Springer, (2017). All Rights Reserved. ;ISSN: 1380-7501 ;EISSN: 1573-7721 ;DOI: 10.1007/s11042-017-4923-3

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8
3-D face recognition: features, databases, algorithms and challenges
3-D face recognition: features, databases, algorithms and challenges
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3-D face recognition: features, databases, algorithms and challenges

The Artificial intelligence review, 2015-10, Vol.44 (3), p.393-441 [Peer Reviewed Journal]

Springer Science+Business Media Dordrecht 2015 ;COPYRIGHT 2015 Springer ;ISSN: 0269-2821 ;EISSN: 1573-7462 ;DOI: 10.1007/s10462-015-9431-0

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9
3-level Confidence Voting Strategy for Dynamic Fusion-Selection of Classifier Ensembles
3-level Confidence Voting Strategy for Dynamic Fusion-Selection of Classifier Ensembles
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3-level Confidence Voting Strategy for Dynamic Fusion-Selection of Classifier Ensembles

Acta cybernetica (Szeged), 2009, Vol.19 (1), p.41-60 [Peer Reviewed Journal]

Copyright Laszlo Nyul Jan 2009 ;ISSN: 0324-721X ;DOI: 10.14232/actacyb.19.1.2009.4

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10
3D cerebral MR image segmentation using multiple-classifier system
3D cerebral MR image segmentation using multiple-classifier system
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3D cerebral MR image segmentation using multiple-classifier system

Medical & biological engineering & computing, 2017-03, Vol.55 (3), p.353-364 [Peer Reviewed Journal]

International Federation for Medical and Biological Engineering 2016 ;Medical & Biological Engineering & Computing is a copyright of Springer, 2017. ;ISSN: 0140-0118 ;EISSN: 1741-0444 ;DOI: 10.1007/s11517-016-1483-z ;PMID: 27207464

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11
3D MAPPING OF BENTHIC HABITAT USING XGBOOST AND STRUCTURE FROM MOTION PHOTOGRAMMETRY
3D MAPPING OF BENTHIC HABITAT USING XGBOOST AND STRUCTURE FROM MOTION PHOTOGRAMMETRY
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3D MAPPING OF BENTHIC HABITAT USING XGBOOST AND STRUCTURE FROM MOTION PHOTOGRAMMETRY

ISPRS annals of the photogrammetry, remote sensing and spatial information sciences, 2023-01, Vol.X-1/W1-2023, p.1131-1136 [Peer Reviewed Journal]

2023. This work is published 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. ;ISSN: 2194-9050 ;ISSN: 2194-9042 ;EISSN: 2194-9050 ;DOI: 10.5194/isprs-annals-X-1-W1-2023-1131-2023

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12
3D palmprint identification using blocked histogram and improved sparse representation-based classifier
3D palmprint identification using blocked histogram and improved sparse representation-based classifier
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3D palmprint identification using blocked histogram and improved sparse representation-based classifier

Neural computing & applications, 2020-08, Vol.32 (16), p.12547-12560 [Peer Reviewed Journal]

Springer-Verlag London Ltd., part of Springer Nature 2020 ;Springer-Verlag London Ltd., part of Springer Nature 2020. ;ISSN: 0941-0643 ;EISSN: 1433-3058 ;DOI: 10.1007/s00521-020-04711-2

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13
3D Shape Classification Using a Single View
3D Shape Classification Using a Single View
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3D Shape Classification Using a Single View

IEEE access, 2020, Vol.8, p.200812-200822 [Peer Reviewed Journal]

Copyright The Institute of Electrical and Electronics Engineers, Inc. (IEEE) 2020 ;ISSN: 2169-3536 ;EISSN: 2169-3536 ;DOI: 10.1109/ACCESS.2020.3035583 ;CODEN: IAECCG

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14
A 24-h forecast of ozone peaks and exceedance levels using neural classifiers and weather predictions
A 24-h forecast of ozone peaks and exceedance levels using neural classifiers and weather predictions
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A 24-h forecast of ozone peaks and exceedance levels using neural classifiers and weather predictions

Environmental modelling & software : with environment data news, 2007-09, Vol.22 (9), p.1261-1269 [Peer Reviewed Journal]

Distributed under a Creative Commons Attribution 4.0 International License ;ISSN: 1364-8152 ;EISSN: 1873-6726 ;DOI: 10.1016/j.envsoft.2006.08.002

Digital Resources/Online E-Resources

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15
A 3D nodule candidate detection method supported by hybrid features to reduce false positives in lung nodule detection
A 3D nodule candidate detection method supported by hybrid features to reduce false positives in lung nodule detection
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A 3D nodule candidate detection method supported by hybrid features to reduce false positives in lung nodule detection

Multimedia tools and applications, 2019-09, Vol.78 (18), p.26287-26311 [Peer Reviewed Journal]

Springer Science+Business Media, LLC, part of Springer Nature 2019 ;Multimedia Tools and Applications is a copyright of Springer, (2019). All Rights Reserved. ;ISSN: 1380-7501 ;EISSN: 1573-7721 ;DOI: 10.1007/s11042-019-07819-3

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16
A-phase classification using convolutional neural networks
A-phase classification using convolutional neural networks
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A-phase classification using convolutional neural networks

Medical & biological engineering & computing, 2020-05, Vol.58 (5), p.1003-1014 [Peer Reviewed Journal]

International Federation for Medical and Biological Engineering 2020 ;International Federation for Medical and Biological Engineering 2020. ;ISSN: 0140-0118 ;EISSN: 1741-0444 ;DOI: 10.1007/s11517-020-02144-6 ;PMID: 32124224

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17
A-SMOTE: A New Preprocessing Approach for Highly Imbalanced Datasets by Improving SMOTE
A-SMOTE: A New Preprocessing Approach for Highly Imbalanced Datasets by Improving SMOTE
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A-SMOTE: A New Preprocessing Approach for Highly Imbalanced Datasets by Improving SMOTE

International journal of computational intelligence systems, 2019-01, Vol.12 (2), p.1412-1422 [Peer Reviewed Journal]

The Authors 2019 ;ISSN: 1875-6891 ;ISSN: 1875-6883 ;EISSN: 1875-6883 ;DOI: 10.2991/ijcis.d.191114.002

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18
Abnormal Event Detection via Feature Expectation Subgraph Calibrating Classification in Video Surveillance Scenes
Abnormal Event Detection via Feature Expectation Subgraph Calibrating Classification in Video Surveillance Scenes
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Abnormal Event Detection via Feature Expectation Subgraph Calibrating Classification in Video Surveillance Scenes

IEEE access, 2020, Vol.8, p.97564-97575 [Peer Reviewed Journal]

Copyright The Institute of Electrical and Electronics Engineers, Inc. (IEEE) 2020 ;ISSN: 2169-3536 ;EISSN: 2169-3536 ;DOI: 10.1109/ACCESS.2020.2997357 ;CODEN: IAECCG

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19
Accelerating Crop Yield: Multisensor Data Fusion and Machine Learning for Agriculture Text Classification
Accelerating Crop Yield: Multisensor Data Fusion and Machine Learning for Agriculture Text Classification
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Accelerating Crop Yield: Multisensor Data Fusion and Machine Learning for Agriculture Text Classification

IEEE access, 2023-01, Vol.11, p.1-1 [Peer Reviewed Journal]

Copyright The Institute of Electrical and Electronics Engineers, Inc. (IEEE) 2023 ;ISSN: 2169-3536 ;EISSN: 2169-3536 ;DOI: 10.1109/ACCESS.2023.3249205 ;CODEN: IAECCG

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20
Accelerating FCM neural network classifier using graphics processing units with CUDA
Accelerating FCM neural network classifier using graphics processing units with CUDA
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Accelerating FCM neural network classifier using graphics processing units with CUDA

Applied intelligence (Dordrecht, Netherlands), 2014, Vol.40 (1), p.143-153 [Peer Reviewed Journal]

Springer Science+Business Media New York 2013 ;2015 INIST-CNRS ;Springer Science+Business Media New York 2014 ;ISSN: 0924-669X ;EISSN: 1573-7497 ;DOI: 10.1007/s10489-013-0450-8

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