Deep Neural Networks and Explainable Machine Learning
From a general perspective, the most impressive results [1, 6] in Machine Learning have been recently obtained via black-box models, being Deep Neural Networks (DNNs) the major player in the game.
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| Published in | Fuzzy Logic and Applications Vol. 11291; pp. 253 - 256 |
|---|---|
| Main Authors | , |
| Format | Book Chapter |
| Language | English |
| Published |
Switzerland
Springer International Publishing AG
2019
Springer International Publishing |
| Series | Lecture Notes in Computer Science |
| Subjects | |
| Online Access | Get full text |
| ISBN | 9783030125431 3030125432 |
| ISSN | 0302-9743 1611-3349 |
| DOI | 10.1007/978-3-030-12544-8_23 |
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| Abstract | From a general perspective, the most impressive results [1, 6] in Machine Learning have been recently obtained via black-box models, being Deep Neural Networks (DNNs) the major player in the game. |
|---|---|
| AbstractList | From a general perspective, the most impressive results [1, 6] in Machine Learning have been recently obtained via black-box models, being Deep Neural Networks (DNNs) the major player in the game. |
| Author | Maratea, Antonio Ferone, Alessio |
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| Copyright | Springer Nature Switzerland AG 2019 |
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| DOI | 10.1007/978-3-030-12544-8_23 |
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| Editor | Giove, Silvio Fullér, Robert Masulli, Francesco |
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| PublicationSeriesSubtitle | Lecture Notes in Artificial Intelligence |
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| PublicationSubtitle | 12th International Workshop, WILF 2018, Genoa, Italy, September 6-7, 2018, Revised Selected Papers |
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| SubjectTerms | Deep Neural Networks Granular Computing XAI |
| Title | Deep Neural Networks and Explainable Machine Learning |
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