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Libros sobre el tema "Learning with Limited Data"

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Autor: Grafiati
Publicado: 18 de mayo de 2024

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1

Zamzmi, Ghada, Sameer Antani, Ulas Bagci, Marius George Linguraru, Sivaramakrishnan Rajaraman y Zhiyun Xue, eds. Medical Image Learning with Limited and Noisy Data. Cham: Springer Nature Switzerland, 2022. http://dx.doi.org/10.1007/978-3-031-16760-7.

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2

Xue, Zhiyun, Sameer Antani, Ghada Zamzmi, Feng Yang, Sivaramakrishnan Rajaraman, Sharon Xiaolei Huang, Marius George Linguraru y Zhaohui Liang, eds. Medical Image Learning with Limited and Noisy Data. Cham: Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-44917-8.

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3

Fisher, Doug y Hans-J. Lenz, eds. Learning from Data. New York, NY: Springer New York, 1996. http://dx.doi.org/10.1007/978-1-4612-2404-4.

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4

Big learning data. Alexandria, VA: ASTD Press, 2014.

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5

Hartemink, Alfred E., Alex McBratney y Maria de Lourdes Mendonça-Santos, eds. Digital Soil Mapping with Limited Data. Dordrecht: Springer Netherlands, 2008. http://dx.doi.org/10.1007/978-1-4020-8592-5.

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6

1964-, Hartemink Alfred E., McBratney A. B y Mendonça-Santos Maria de Lourdes, eds. Digital soil mapping with limited data. Dordrecht: Springer, 2008.

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7

Velleman, Paul F. Learning data analysis with Data desk. New York: W.H. Freeman, 1993.

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8

Learning data analysis with Data desk. New York: W.H. Freeman, 1989.

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9

VNU Entertainment Media UK Limited and Book Data Limited: A report on the acquisition by VNU Entertainment Media UK Limited of Book Data Limited. London: Stationery Office, 2003.

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10

Dean, Jared. Big Data, Data Mining, and Machine Learning. Hoboken, NJ, USA: John Wiley & Sons, Inc., 2014. http://dx.doi.org/10.1002/9781118691786.

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11

Gama, João y Mohamed Medhat Gaber, eds. Learning from Data Streams. Berlin, Heidelberg: Springer Berlin Heidelberg, 2007. http://dx.doi.org/10.1007/3-540-73679-4.

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12

Nagabhushan, P., D. S. Guru, B. H. Shekar y Y. H. Sharath Kumar, eds. Data Analytics and Learning. Singapore: Springer Singapore, 2019. http://dx.doi.org/10.1007/978-981-13-2514-4.

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13

Peck, Roxy. Statistics: Learning from data. Australia: Brooks/Cole, Cengage Learning, 2014.

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14

Guru, D. S., N. Vinay Kumar y Mohammed Javed, eds. Data Analytics and Learning. Singapore: Springer Nature Singapore, 2024. http://dx.doi.org/10.1007/978-981-99-6346-1.

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15

Beth, Mandinach Ellen, Honey Margaret y Center for Children and Technology (Education Development Center), eds. Data-driven school improvement: Linking data and learning. New York: Teachers College Press, 2008.

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16

Australian Society for Fish Biology. Workshop. Towards sustainability of data-limited multi-sector fisheries. Editado por Western Australia. Dept. of Fisheries. North Beach, W.A: Dept. of Fisheries, 2003.

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17

Golan, Amos. Maximum entropy econometrics: Robust estimation with limited data. Chichester [England]: Wiley, 1996.

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18

M, Gass Susan y Selinker Larry 1937-, eds. Second language learning data analysis. Hillsdale, N.J: Lawrence Erlbaum Assoc., 1994.

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19

Nikolenko, Sergey I. Synthetic Data for Deep Learning. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-75178-4.

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20

Acharjya, Debi Prasanna, Anirban Mitra y Noor Zaman, eds. Deep Learning in Data Analytics. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-75855-4.

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21

van der Laan, Mark J. y Sherri Rose. Targeted Learning in Data Science. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-65304-4.

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22

Gammerman, Alexander, Vladimir Vovk y Harris Papadopoulos, eds. Statistical Learning and Data Sciences. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-17091-6.

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23

Fernández, Alberto, Salvador García, Mikel Galar, Ronaldo C. Prati, Bartosz Krawczyk y Francisco Herrera. Learning from Imbalanced Data Sets. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-98074-4.

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24

ElAtia, Samira, Donald Ipperciel y Osmar R. Zaïane, eds. Data Mining and Learning Analytics. Hoboken, NJ, USA: John Wiley & Sons, Inc., 2016. http://dx.doi.org/10.1002/9781118998205.

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25

Greselin, Francesca, Laura Deldossi, Luca Bagnato y Maurizio Vichi, eds. Statistical Learning of Complex Data. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-21140-0.

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26

Gürsakal, Necmi, Sadullah Çelik y Esma Birişçi. Synthetic Data for Deep Learning. Berkeley, CA: Apress, 2022. http://dx.doi.org/10.1007/978-1-4842-8587-9.

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27

Antonella, Sorace, Selinker Larry 1937- y Selinker Larry 1937-, eds. Second language learning data analysis. 2a ed. Mahwah, N.J: Erlbaum, 1998.

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28

Statistical Learning for Process Data. [New York, N.Y.?]: [publisher not identified], 2021.

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29

Friedman, Craig. Utility-based learning from data. Boca Raton: Chapman & Hall/CRC, 2010.

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30

Statistical learning and data science. Boca Raton: CRC Press, 2012.

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31

El Namaki, MSS y Pooja Sharma, eds. Management of Data in AI Age. CSMFL Publications, 2020. http://dx.doi.org/10.46679/isbn9788194848349.

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This book is a compilation of contributed works on management of data in the age of artificial intelligence. The AI technologies have changed the way the businesses do manage themselves in modern times. It becomes much more important to manage the data a business owns when the same can be collated and used by the allied AI technologies for forming business decisions. This book highlights how AI and machine learning can help businesses categorise and manage their organizational data. The book introduces how small businesses can benefit from AI technologies for their data management with limited budgets. The book advocates for making AI processes to be core part of consumer experience and support management within the businesses. As a unique feature, this book also goes to make an awareness as to how human brain can use AI’s deep learning capabilities to make reflective decisions. The book also introduces as to how big data and big data analytics can help agriculture and farm management sector. It is hoped that the readership will find this book useful in the areas of big data management, machine learning and data decisions, AI technologies for small businesses, usage of AI in emerging sectors and those areas where data needs to managed in an environment of automation.
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32

Bagci, Ulas, Sivaramakrishnan Rajaraman, Marius George Linguraru, Sameer Antani y Ghada Zamzmi. Medical Image Learning with Limited and Noisy Data: First International Workshop, MILLanD 2022, Held in Conjunction with MICCAI 2022, Singapore, September 22, 2022, Proceedings. Springer, 2022.

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33

Dzakiria, Hisham. Pragmatic Approach to Qualitative Case Study Research Learning by Doing: A Case of Distance Learning Research in Malaysia. UUM Press, 2008. http://dx.doi.org/10.32890/9789833827718.

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This book for anyone who wants to undertake an engaging, satisfying, productive, and a more successful career as a qualitative researcher. This book is intended to contribute to the popularisation of qualitative research in Malaysia. Qualitative studies in educational research are very limited in many countries; and to date, there has been very little work done using this form of educational inquiry in Malaysia. The dominant tradition has followed the positivist paradigm. A qualitative case study offers a different approach and generates a range of information of different qualities from that obtained using traditional approaches. This book provides both the theories and practical practices to undertake a qualitative study. The conception of this book began from the assumption that our world is interpreted through language as means of communication and understanding. Writing narratives of experience is becoming a common way of describing how people make sense of their experience or problems at hand. At the root of the naturalistic inquiry exhibited in this book, is an interest in understanding the experience of learners and the meanings they make of the distance learning experience at Universiti Utara Malaysia (UUM). This study was designed as a single case focusing on distance learners of different backgrounds in UUM.
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34

Glenberg, Arthur, Matthew Andrzejewski, Herman Fernando, Jas Kalsi, Asif Muneer y Hashim Ahmed. Learning From Data. Routledge, 2007. http://dx.doi.org/10.4324/9780203809631.

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35

Glenberg, Arthur M. y Matthew E. Andrzejewski. Learning From Data. Routledge, 2007. http://dx.doi.org/10.4324/9780429235139.

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36

Cherkassky, Vladimir y Filip M. Mulier. Learning from Data. Wiley & Sons, Incorporated, John, 2007.

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37

Masie, Elliott. Big Learning Data. American Society for Training & Development, 2013.

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38

Mendonça-Santos, Maria de Lourdes, Robert J. Ahrens, Alfred E. Hartemink y Alex B. McBratney. Digital Soil Mapping with Limited Data. Springer, 2008.

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39

Mendonça-Santos, Maria de Lourdes, A. E. Hartemink, Robert J. Ahrens y Alex B. McBratney. Digital Soil Mapping with Limited Data. Springer Netherlands, 2010.

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40

Structural Damage Identification from Limited Measurement Data. Storming Media, 1996.

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41

Adaptive Hierarchial Classification with Limited Training Data. Storming Media, 2002.

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42

Research, ASTD. Big Data, Better Learning: How Big Data Is Affecting Organizational Learning. American Society for Training & Development, 2014.

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43

Peck, Roxy y Chris Olsen. Statistics: Learning from Data. Brooks/Cole, 2013.

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44

Peck, Roxy. Statistics: Learning from Data. Brooks/Cole, 2017.

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45

Learning Qlikview Data Visualization. Packt Publishing, Limited, 2013.

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46

Peck, Roxy. Statistics: Learning from Data. Cengage Learning, 2023.

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47

Learning from Data "Comp. Lawrence Erlbaum Associates, 1996.

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48

Learning Haskell Data Analysis. Packt Publishing, Limited, 2015.

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49

Learning Python Data Visualization. Packt Publishing, 2014.

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50

Research, ASTD y i4cp. Big Data, Better Learning? American Society for Training & Development, 2014.

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