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Image Augmentation Using Radial Transform for Training Deep Neural Networks
Image Augmentation Using Radial Transform for Training Deep Neural Networks
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Image Augmentation Using Radial Transform for Training Deep Neural Networks

Salehinejad, Hojjat ; Valaee, Shahrokh ; Dowdell, Tim ; Barfett, Joseph

2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2018, p.3016-3020

IEEE

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Incremental classification learning for anomaly detection in medical images
Incremental classification learning for anomaly detection in medical images
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Incremental classification learning for anomaly detection in medical images

Giritharan, Balathasan ; Yuan, Xiaohui ; Liu, Jianguo

SPIE 2009

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3
Towards Effective Classification of Imbalanced Data with Convolutional Neural Networks
Towards Effective Classification of Imbalanced Data with Convolutional Neural Networks
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Towards Effective Classification of Imbalanced Data with Convolutional Neural Networks

Raj, Vidwath ; Magg, Sven ; Wermter, Stefan

Artificial Neural Networks in Pattern Recognition, p.150-162 [Periódico revisado por pares]

Cham: Springer International Publishing

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4
AdaBoost-SVM for Electrical Theft Detection and GRNN for Stealing Time Periods Identification
AdaBoost-SVM for Electrical Theft Detection and GRNN for Stealing Time Periods Identification
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AdaBoost-SVM for Electrical Theft Detection and GRNN for Stealing Time Periods Identification

Wu, Rongli ; Wang, Liming ; Hu, Tianyu

IECON 2018 - 44th Annual Conference of the IEEE Industrial Electronics Society, 2018, p.3073-3078

IEEE

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5
A First Approach to Deal with Imbalance in Multi-label Datasets
A First Approach to Deal with Imbalance in Multi-label Datasets
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A First Approach to Deal with Imbalance in Multi-label Datasets

Charte, Francisco ; Rivera, Antonio ; del Jesus, María José ; Herrera, Francisco

Hybrid Artificial Intelligent Systems, p.150-160 [Periódico revisado por pares]

Berlin, Heidelberg: Springer Berlin Heidelberg

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6
F-Measure as the Error Function to Train Neural Networks
F-Measure as the Error Function to Train Neural Networks
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F-Measure as the Error Function to Train Neural Networks

Pastor-Pellicer, Joan ; Zamora-Martínez, Francisco ; España-Boquera, Salvador ; Castro-Bleda, María José

Advances in Computational Intelligence, p.376-384 [Periódico revisado por pares]

Berlin, Heidelberg: Springer Berlin Heidelberg

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7
Comparative analysis on imbalanced multi-class classification for malware samples using CNN
Comparative analysis on imbalanced multi-class classification for malware samples using CNN
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Comparative analysis on imbalanced multi-class classification for malware samples using CNN

Arwa Alzammam ; Hamad Binsalleeh ; Basil AsSdhan ; Kostas Kyriakopoulos ; Sangarapillai Lambotharan

2020

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8
Two-Class with Oversampling Versus One-Class Classification for Microarray Datasets
Two-Class with Oversampling Versus One-Class Classification for Microarray Datasets
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Two-Class with Oversampling Versus One-Class Classification for Microarray Datasets

Pérez-Sánchez, Beatriz ; Fontenla-Romero, Oscar ; Sánchez-Maroño, Noelia

Artificial Neural Networks and Machine Learning – ICANN 2016, p.398-405 [Periódico revisado por pares]

Cham: Springer International Publishing

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9
CTCHAID: Extending the Application of the Consolidation Methodology
CTCHAID: Extending the Application of the Consolidation Methodology
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CTCHAID: Extending the Application of the Consolidation Methodology

Ibarguren, Igor ; Pérez, Jesús María ; Muguerza, Javier

Progress in Artificial Intelligence, p.572-577 [Periódico revisado por pares]

Cham: Springer International Publishing

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10
Learning and Utilizing a Pool of Features in Non-negative Matrix Factorization
Learning and Utilizing a Pool of Features in Non-negative Matrix Factorization
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Learning and Utilizing a Pool of Features in Non-negative Matrix Factorization

Yoshida, Tetsuya

Active Media Technology, p.96-105 [Periódico revisado por pares]

Cham: Springer International Publishing

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