Machine learning algorithms for engineering applications :: future trends and research directions /
"Machine learning is a vital part of numerous academic and financial applications, in areas ranging from health care and treatment to finding relevant information in social networks. Large organizations thoughtfully apply machine learning algorithms with extensive research teams. The purpose of...
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| Other Authors: | , , , |
|---|---|
| Format: | Electronic eBook |
| Language: | English |
| Published: |
New York :
Nova Science Publishers, Inc.,
[2022]
|
| Series: | Advances in data science and computing technologies
|
| Subjects: | |
| Online Access: | DE-862 DE-863 |
| Summary: | "Machine learning is a vital part of numerous academic and financial applications, in areas ranging from health care and treatment to finding relevant information in social networks. Large organizations thoughtfully apply machine learning algorithms with extensive research teams. The purpose of this book is to provide an intellectual introduction to statistical or machine learning (ML) techniques for those that would not normally be exposed to such approaches during their typical required statistical exercise. Statistical analysis is an integral part of machine learning and can be described as a form of it, often even utilizing well-known and familiar techniques, that has a different focus than traditional analytical practice in applied disciplines. The key notion is that flexible, automatic approaches are used to detect patterns within the data, with a primary focus on making predictions on future data"-- |
| Physical Description: | 1 online resource (xiv, 214 pages) : illustrations (chiefly color), color map. |
| Bibliography: | Includes bibliographical references and index. |
| ISBN: | 9798886970869 |
Staff View
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| 588 | |a Description based on online resource; title from digital title page (viewed on August 22, 2022). | ||
| 650 | 0 | |a Engineering |x Data processing. |0 http://id.loc.gov/authorities/subjects/sh85043180 | |
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| 700 | 1 | |a Astya, Parmanand, |e editor. | |
| 700 | 1 | |a Chakraborty, Sudeshna, |e editor. | |
| 700 | 1 | |a Pooja, |e editor. | |
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| illustrated | Illustrated |
| indexdate | 2025-04-11T08:47:58Z |
| institution | BVB |
| isbn | 9798886970869 |
| language | English |
| lccn | 2022028068 |
| oclc_num | 1331705982 |
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| physical | 1 online resource (xiv, 214 pages) : illustrations (chiefly color), color map. |
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| publishDate | 2022 |
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| series2 | Advances in data science and computing technologies |
| spelling | Machine learning algorithms for engineering applications : future trends and research directions / Prasenjit Chatterjee, Parmanand Astya, Sudeshna Chakraborty and Pooja, editors. New York : Nova Science Publishers, Inc., [2022] 1 online resource (xiv, 214 pages) : illustrations (chiefly color), color map. text txt rdacontent computer c rdamedia online resource cr rdacarrier Advances in data science and computing technologies Includes bibliographical references and index. "Machine learning is a vital part of numerous academic and financial applications, in areas ranging from health care and treatment to finding relevant information in social networks. Large organizations thoughtfully apply machine learning algorithms with extensive research teams. The purpose of this book is to provide an intellectual introduction to statistical or machine learning (ML) techniques for those that would not normally be exposed to such approaches during their typical required statistical exercise. Statistical analysis is an integral part of machine learning and can be described as a form of it, often even utilizing well-known and familiar techniques, that has a different focus than traditional analytical practice in applied disciplines. The key notion is that flexible, automatic approaches are used to detect patterns within the data, with a primary focus on making predictions on future data"-- Provided by publisher. Description based on online resource; title from digital title page (viewed on August 22, 2022). Engineering Data processing. http://id.loc.gov/authorities/subjects/sh85043180 Artificial intelligence. http://id.loc.gov/authorities/subjects/sh85008180 Machine learning. http://id.loc.gov/authorities/subjects/sh85079324 Ingénierie Informatique. Intelligence artificielle. Apprentissage automatique. artificial intelligence. aat Artificial intelligence fast Engineering Data processing fast Machine learning fast Chatterjee, Prasenjit, 1982- editor. https://id.oclc.org/worldcat/entity/E39PBJj4tfp3rWW4WkWdFr4H4q http://id.loc.gov/authorities/names/n2018066742 Astya, Parmanand, editor. Chakraborty, Sudeshna, editor. Pooja, editor. Print version: Machine learning algorithms for engineering applications New York : Nova Science Publishers, [2022] 9781685074494 (DLC) 2022028067 |
| spellingShingle | Machine learning algorithms for engineering applications : future trends and research directions / Engineering Data processing. http://id.loc.gov/authorities/subjects/sh85043180 Artificial intelligence. http://id.loc.gov/authorities/subjects/sh85008180 Machine learning. http://id.loc.gov/authorities/subjects/sh85079324 Ingénierie Informatique. Intelligence artificielle. Apprentissage automatique. artificial intelligence. aat Artificial intelligence fast Engineering Data processing fast Machine learning fast |
| subject_GND | http://id.loc.gov/authorities/subjects/sh85043180 http://id.loc.gov/authorities/subjects/sh85008180 http://id.loc.gov/authorities/subjects/sh85079324 |
| title | Machine learning algorithms for engineering applications : future trends and research directions / |
| title_auth | Machine learning algorithms for engineering applications : future trends and research directions / |
| title_exact_search | Machine learning algorithms for engineering applications : future trends and research directions / |
| title_full | Machine learning algorithms for engineering applications : future trends and research directions / Prasenjit Chatterjee, Parmanand Astya, Sudeshna Chakraborty and Pooja, editors. |
| title_fullStr | Machine learning algorithms for engineering applications : future trends and research directions / Prasenjit Chatterjee, Parmanand Astya, Sudeshna Chakraborty and Pooja, editors. |
| title_full_unstemmed | Machine learning algorithms for engineering applications : future trends and research directions / Prasenjit Chatterjee, Parmanand Astya, Sudeshna Chakraborty and Pooja, editors. |
| title_short | Machine learning algorithms for engineering applications : |
| title_sort | machine learning algorithms for engineering applications future trends and research directions |
| title_sub | future trends and research directions / |
| topic | Engineering Data processing. http://id.loc.gov/authorities/subjects/sh85043180 Artificial intelligence. http://id.loc.gov/authorities/subjects/sh85008180 Machine learning. http://id.loc.gov/authorities/subjects/sh85079324 Ingénierie Informatique. Intelligence artificielle. Apprentissage automatique. artificial intelligence. aat Artificial intelligence fast Engineering Data processing fast Machine learning fast |
| topic_facet | Engineering Data processing. Artificial intelligence. Machine learning. Ingénierie Informatique. Intelligence artificielle. Apprentissage automatique. artificial intelligence. Artificial intelligence Engineering Data processing Machine learning |
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