| 書目名稱 | Representation in Machine Learning |
| 編輯 | M. N. Murty,M. Avinash |
| 視頻video | http://file.papertrans.cn/828/827434/827434.mp4 |
| 概述 | Provides comprehensive coverage of Machine Learning representation techniques.Demonstrates the performance of various representation techniques using benchmark datasets.Illustrates the content using e |
| 叢書名稱 | SpringerBriefs in Computer Science |
| 圖書封面 |  |
| 描述 | .This book provides a concise but comprehensive guide to representation, which forms the core of Machine Learning (ML). State-of-the-art practical applications involve a number of challenges for the analysis of high-dimensional data. Unfortunately, many popular ML algorithms fail to perform, in both theory and practice, when they are confronted with the huge size of the underlying data. Solutions to this problem are aptly covered in the book..In addition, the book covers a wide range of representation techniques that are important for academics and ML practitioners alike, such as Locality Sensitive Hashing (LSH), Distance Metrics and Fractional Norms, Principal Components (PCs), Random Projections and Autoencoders. Several experimental results are provided in the book to demonstrate the discussed techniques’ effectiveness.. |
| 出版日期 | Book 2023 |
| 關(guān)鍵詞 | Representation; Dimensionality Reduction; Machine Learning; Data Mining; Autoencoder; Locality Sensitive |
| 版次 | 1 |
| doi | https://doi.org/10.1007/978-981-19-7908-8 |
| isbn_softcover | 978-981-19-7907-1 |
| isbn_ebook | 978-981-19-7908-8Series ISSN 2191-5768 Series E-ISSN 2191-5776 |
| issn_series | 2191-5768 |
| copyright | The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023 |
|Archiver|手機(jī)版|小黑屋|
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