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A general architecture for supervised classification of multivariate time series [electronic resource] / Mohammed Waleed, Kadous.
Author / Creator:Kadous, Mohammed Waleed.
Publisher:[Sydney] : University of New South Wales, School of Computer Science and Engineering, 1998.
Date:1998.
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Books
A-Z of reflective practice / Fiona Timmins.
Author / Creator:Timmins, Fiona, 1967- author.
Publisher:London ; New York, NY : Macmillan Education Palgrave, 2015.
Call Numbers:G 2015/3337
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Books
Digital neural networks / S.Y. Kung.
Author / Creator:Kung, S. Y. (Sun Yuan)
Publisher:Englewood Cliffs, N.J. : PTR Prentice Hall, c1993.
Call Numbers:T0375177
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Pictures | Photographs
File 5: Douglas Holleley exhibition prints of E. A. Hopkins glass plate and film negatives, 1937-1966, 1995
Author / Creator:Hopkins, E. A., d. 1992
Date:1937-1966, 1995
Call Numbers:PXD 1427
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ArticlesPeer ReviewedFull Text
Supervised machine learning enables non-invasive lesion characterization in primary prostate cancer with [68Ga]Ga-PSMA-11 PET/MRI
Author / Creator:Papp, L. ; Spielvogel, C. P. ; Grubmüller, B. ; Grahovac, M. ; Krajnc, D. ; Ecsedi, B. ; Sareshgi, R. A.M. ; Mohamad, D. ; Hamboeck, M. ; Rausch, I. ; Mitterhauser, M. ; Wadsak, W. ; Haug, A. R. ; Kenner, L. ; Mazal, P. ; Susani, M. ; Hartenbach, S. ; Baltzer, P. ; Helbich, T. H. ; Kramer, G. ; Shariat, S.F. ; Beyer, T. ; Hartenbach, M. ; Hacker, M.
Publisher:Berlin/Heidelberg: Springer Berlin Heidelberg
Journal title:European journal of nuclear medicine and molecular imaging, 2021-06, Vol.48 (6), p.1795-1805
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ArticlesPeer ReviewedFull Text
Weakly supervised machine learning
Author / Creator:Ren, Zeyu ; Wang, Shuihua ; Zhang, Yudong
Publisher:Beijing: John Wiley & Sons, Inc
Journal title:CAAI Transactions on Intelligence Technology, 2023-09, Vol.8 (3), p.549-580
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ArticlesPeer ReviewedFull Text
Comparing different supervised machine learning algorithms for disease prediction
Author / Creator:Uddin, Shahadat ; Khan, Arif ; Hossain, Md Ekramul ; Moni, Mohammad Ali
Publisher:England: BioMed Central Ltd
Journal title:BMC medical informatics and decision making, 2019-12, Vol.19 (1), p.281-281, Article 281
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ArticlesPeer ReviewedFull Text
A Survey on the Explainability of Supervised Machine Learning
Author / Creator:Burkart, Nadia ; Huber, Marco F.
Publisher:San Francisco: AI Access Foundation
Journal title:The Journal of artificial intelligence research, 2021-01, Vol.70, p.245-317
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ArticlesPeer ReviewedFull Text
DOME: recommendations for supervised machine learning validation in biology
Author / Creator:Walsh, Ian ; Fishman, Dmytro ; Garcia-Gasulla, Dario ; Titma, Tiina ; Pollastri, Gianluca ; Harrow, Jennifer ; Psomopoulos, Fotis E. ; Tosatto, Silvio C. E.
Publisher:New York: Nature Publishing Group US
Journal title:Nature methods, 2021-10, Vol.18 (10), p.1122-1127
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ArticlesPeer ReviewedFull Text
Regulating Cryptocurrencies: A Supervised Machine Learning Approach to De-Anonymizing the Bitcoin Blockchain
Author / Creator:Sun Yin, Hao Hua ; Langenheldt, Klaus ; Harlev, Mikkel ; Mukkamala, Raghava Rao ; Vatrapu, Ravi
Publisher:Abingdon: Routledge
Journal title:Journal of management information systems, 2019-01, Vol.36 (1), p.37-73
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ArticlesPeer ReviewedFull Text
Assessment of supervised machine learning methods for fluid flows
Author / Creator:Fukami, Kai ; Fukagata, Koji ; Taira, Kunihiko
Publisher:Berlin/Heidelberg: Springer Berlin Heidelberg
Journal title:Theoretical and computational fluid dynamics, 2020-08, Vol.34 (4), p.497-519
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ArticlesPeer ReviewedFull Text
A performance comparison of supervised machine learning models for Covid-19 tweets sentiment analysis
Author / Creator:Rustam, Furqan ; Khalid, Madiha ; Aslam, Waqar ; Rupapara, Vaibhav ; Mehmood, Arif ; Choi, Gyu Sang
Publisher:United States: Public Library of Science
Journal title:PloS one, 2021-02, Vol.16 (2), p.e0245909-e0245909
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ArticlesPeer ReviewedFull Text
A rigorous and robust quantum speed-up in supervised machine learning
Author / Creator:Liu, Yunchao ; Arunachalam, Srinivasan ; Temme, Kristan
Publisher:London: Nature Publishing Group UK
Journal title:Nature physics, 2021-09, Vol.17 (9), p.1013-1017
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ArticlesPeer ReviewedFull Text
Survey on supervised machine learning techniques for automatic text classification
Author / Creator:Kadhim, Ammar Ismael
Publisher:Dordrecht: Springer Netherlands
Journal title:The Artificial intelligence review, 2019-06, Vol.52 (1), p.273-292
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ArticlesPeer ReviewedFull Text
Risk of bias in studies on prediction models developed using supervised machine learning techniques: systematic review
Author / Creator:Andaur Navarro, Constanza L ; Damen, Johanna A A ; Takada, Toshihiko ; Nijman, Steven W J ; Dhiman, Paula ; Ma, Jie ; Collins, Gary S ; Bajpai, Ram ; Riley, Richard D ; Moons, Karel G M ; Hooft, Lotty
Publisher:England: British Medical Journal Publishing Group
Journal title:BMJ (Online), 2021-10, Vol.375, p.n2281-n2281
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ArticlesPeer ReviewedFull Text
Efficient Water Quality Prediction Using Supervised Machine Learning
Author / Creator:Ahmed, Umair ; Mumtaz, Rafia ; Anwar, Hirra ; Shah, Asad A. ; Irfan, Rabia ; García-Nieto, José
Publisher:Basel: MDPI AG
Journal title:Water (Basel), 2019-11, Vol.11 (11), p.2210
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ArticlesPeer ReviewedFull Text
Use of Supervised Machine Learning for GNSS Signal Spoofing Detection with Validation on Real-World Meaconing and Spoofing Data-Part I
Author / Creator:Semanjski, Silvio ; Semanjski, Ivana ; De Wilde, Wim ; Muls, Alain
Publisher:Switzerland: MDPI
Journal title:Sensors (Basel, Switzerland), 2020-02, Vol.20 (4), p.1171
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ArticlesPeer ReviewedFull Text
Comparative Motor Pre-clinical Assessment in Parkinson’s Disease Using Supervised Machine Learning Approaches
Author / Creator:Rovini, Erika ; Maremmani, Carlo ; Moschetti, Alessandra ; Esposito, Dario ; Cavallo, Filippo
Publisher:New York: Springer US
Journal title:Annals of biomedical engineering, 2018-12, Vol.46 (12), p.2057-2068
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ArticlesPeer ReviewedFull Text
Self-supervised machine learning pushes the sensitivity limit in label-free detection of single proteins below 10 kDa
Author / Creator:Dahmardeh, Mahyar ; Mirzaalian Dastjerdi, Houman ; Mazal, Hisham ; Köstler, Harald ; Sandoghdar, Vahid
Publisher:New York: Nature Publishing Group US
Journal title:Nature methods, 2023-03, Vol.20 (3), p.442-447
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ArticlesPeer ReviewedFull Text
Supervised machine learning for audio emotion recognition: Enhancing film sound design using audio features, regression models and artificial neural networks
Author / Creator:Cunningham, Stuart ; Ridley, Harrison ; Weinel, Jonathan ; Picking, Richard
Publisher:London: Springer London
Journal title:Personal and ubiquitous computing, 2021-08, Vol.25 (4), p.637-650
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A general architecture for supervised classification of multivariate time series [electronic resource] / Mohammed Waleed, Kadous.

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A-Z of reflective practice / Fiona Timmins.

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Digital neural networks / S.Y. Kung.

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File 5: Douglas Holleley exhibition prints of E. A. Hopkins glass plate and film negatives, 1937-1966, 1995

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Supervised machine learning enables non-invasive lesion characterization in primary prostate cancer with [68Ga]Ga-PSMA-11 PET/MRI

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Weakly supervised machine learning

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Comparing different supervised machine learning algorithms for disease prediction

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A Survey on the Explainability of Supervised Machine Learning

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DOME: recommendations for supervised machine learning validation in biology

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Regulating Cryptocurrencies: A Supervised Machine Learning Approach to De-Anonymizing the Bitcoin Blockchain

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Assessment of supervised machine learning methods for fluid flows

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A performance comparison of supervised machine learning models for Covid-19 tweets sentiment analysis

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A rigorous and robust quantum speed-up in supervised machine learning

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Survey on supervised machine learning techniques for automatic text classification

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Risk of bias in studies on prediction models developed using supervised machine learning techniques: systematic review

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Efficient Water Quality Prediction Using Supervised Machine Learning

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Use of Supervised Machine Learning for GNSS Signal Spoofing Detection with Validation on Real-World Meaconing and Spoofing Data-Part I

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Comparative Motor Pre-clinical Assessment in Parkinson’s Disease Using Supervised Machine Learning Approaches

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Self-supervised machine learning pushes the sensitivity limit in label-free detection of single proteins below 10 kDa

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Supervised machine learning for audio emotion recognition: Enhancing film sound design using audio features, regression models and artificial neural networks

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