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Record Nr. |
UNINA9910483445703321 |
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Titolo |
Recent Trends in Learning From Data : Tutorials from the INNS Big Data and Deep Learning Conference (INNSBDDL2019) / / edited by Luca Oneto, Nicolò Navarin, Alessandro Sperduti, Davide Anguita |
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Pubbl/distr/stampa |
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Cham : , : Springer International Publishing : , : Imprint : Springer, , 2020 |
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ISBN |
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Edizione |
[1st ed. 2020.] |
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Descrizione fisica |
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1 online resource (vii, 221 pages) |
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Collana |
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Studies in Computational Intelligence, , 1860-949X ; ; 896 |
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Disciplina |
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Soggetti |
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Computational intelligence |
Machine learning |
Engineering—Data processing |
Computational Intelligence |
Machine Learning |
Data Engineering |
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Lingua di pubblicazione |
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Formato |
Materiale a stampa |
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Livello bibliografico |
Monografia |
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Nota di contenuto |
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Introduction: Recent Trends in Learning From Data -- Learned data structures -- Deep Randomized Neural Networks -- Tensor Decompositions and Practical Applications -- Deep learning for graphs -- Limitations of Shallow Networks -- Fairness in Machine Learning -- Online Continual Learning on Sequences. |
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Sommario/riassunto |
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This book offers a timely snapshot and extensive practical and theoretical insights into the topic of learning from data. Based on the tutorials presented at the INNS Big Data and Deep Learning Conference, INNSBDDL2019, held on April 16-18, 2019, in Sestri Levante, Italy, the respective chapters cover advanced neural networks, deep architectures, and supervised and reinforcement machine learning models. They describe important theoretical concepts, presenting in detail all the necessary mathematical formalizations, and offer essential guidance on their use in current big data research. |
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