| 书目名称 | Representation in Machine Learning |
| 编辑 | M. N. Murty,M. Avinash |
| 视频video | |
| 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 |
| 丛书名称 | SpringerBriefs in Computer Science |
| 图书封面 |  |
| 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 |
| 关键词 | 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 |
发表于 2025-3-21 17:35:56
