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Introduction to algorithms for data mining and machine learning /
Introduction to Algorithms for Data Mining and Machine Learning introduces the essential ideas behind all key algorithms and techniques for data mining and machine learning, along with optimization techniques. Its strong formal mathematical approach, well selected examples, and practical software re...
| Clasificación: | Libro Electrónico |
|---|---|
| Autor principal: | |
| Formato: | Electrónico eBook |
| Idioma: | Inglés |
| Publicado: |
London, United Kingdom ; San Diego, CA, United States :
Academic Press, an imprint of Elsevier,
[2019]
|
| Temas: | |
| Acceso en línea: | Texto completo |
MARC
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| 003 | OCoLC | ||
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| 008 | 190619s2019 enka ob 001 0 eng d | ||
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| 020 | |a 9780128172179 |q (electronic bk.) | ||
| 020 | |a 0128172177 |q (electronic bk.) | ||
| 020 | |z 9780128172162 |q (print) | ||
| 020 | |z 0128172169 |q (print) | ||
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| 072 | 7 | |a COM |x 000000 |2 bisacsh | |
| 082 | 0 | 4 | |a 006.312 |2 23 |
| 100 | 1 | |a Yang, Xin-She, |e author. | |
| 245 | 1 | 0 | |a Introduction to algorithms for data mining and machine learning / |c Xin-She Yang. |
| 264 | 1 | |a London, United Kingdom ; |a San Diego, CA, United States : |b Academic Press, an imprint of Elsevier, |c [2019] | |
| 300 | |a 1 online resource (viii, 173 pages) : |b illustrations | ||
| 336 | |a text |b txt |2 rdacontent | ||
| 337 | |a computer |b c |2 rdamedia | ||
| 338 | |a online resource |b cr |2 rdacarrier | ||
| 504 | |a Includes bibliographical references (pages 163-170) and index. | ||
| 588 | 0 | |a Online resource; title from digital title page (ScienceDirect, viewed July 14, 2020). | |
| 520 | |a Introduction to Algorithms for Data Mining and Machine Learning introduces the essential ideas behind all key algorithms and techniques for data mining and machine learning, along with optimization techniques. Its strong formal mathematical approach, well selected examples, and practical software recommendations help readers develop confidence in their data modeling skills so they can process and interpret data for classification, clustering, curve-fitting and predictions. Masterfully balancing theory and practice, it is especially useful for those who need relevant, well explained, but not rigorous (proofs based) background theory and clear guidelines for working with big data. | ||
| 505 | 0 | |a Introduction to optimization -- Mathematical foundations -- Optimization algorithms -- Data fitting and regression -- Logistic regression, PCA, LDA, and ICA -- Data mining techniques -- Support vector machine and regression -- Neural networks and deep learning. | |
| 650 | 0 | |a Data mining |x Mathematics. | |
| 650 | 0 | |a Machine learning |x Mathematics. | |
| 650 | 6 | |a Exploration de donn�ees (Informatique) |0 (CaQQLa)201-0300292 |x Math�ematiques. |0 (CaQQLa)201-0380112 | |
| 650 | 6 | |a Apprentissage automatique |0 (CaQQLa)201-0131435 |x Math�ematiques. |0 (CaQQLa)201-0380112 | |
| 650 | 7 | |a COMPUTERS |x General. |2 bisacsh | |
| 650 | 7 | |a Data mining |2 fast |0 (OCoLC)fst00887946 | |
| 650 | 7 | |a Machine learning |2 fast |0 (OCoLC)fst01004795 | |
| 776 | 0 | 8 | |i Print version: |a Yang, Xin-She. |t Introduction to algorithms for data mining and machine learning. |d London : Academic Press, [2019] |z 0128172169 |w (OCoLC)1082187185 |
| 856 | 4 | 0 | |u https://sciencedirect.uam.elogim.com/science/book/9780128172162 |z Texto completo |