Latent Factor Analysis for High-dimensional and Sparse Matrices (Record no. 174626)
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| 000 -LEADER | |
|---|---|
| fixed length control field | 03514nam a22005415i 4500 |
| 001 - CONTROL NUMBER | |
| control field | 978-981-19-6703-0 |
| 003 - CONTROL NUMBER IDENTIFIER | |
| control field | DE-He213 |
| 005 - DATE AND TIME OF LATEST TRANSACTION | |
| control field | 20240423125131.0 |
| 007 - PHYSICAL DESCRIPTION FIXED FIELD--GENERAL INFORMATION | |
| fixed length control field | cr nn 008mamaa |
| 008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION | |
| fixed length control field | 221115s2022 si | s |||| 0|eng d |
| 020 ## - INTERNATIONAL STANDARD BOOK NUMBER | |
| International Standard Book Number | 9789811967030 |
| -- | 978-981-19-6703-0 |
| 024 7# - OTHER STANDARD IDENTIFIER | |
| Standard number or code | 10.1007/978-981-19-6703-0 |
| Source of number or code | doi |
| 050 #4 - LIBRARY OF CONGRESS CALL NUMBER | |
| Classification number | Q336 |
| 072 #7 - SUBJECT CATEGORY CODE | |
| Subject category code | UN |
| Source | bicssc |
| 072 #7 - SUBJECT CATEGORY CODE | |
| Subject category code | COM021000 |
| Source | bisacsh |
| 072 #7 - SUBJECT CATEGORY CODE | |
| Subject category code | UN |
| Source | thema |
| 082 04 - DEWEY DECIMAL CLASSIFICATION NUMBER | |
| Classification number | 005.7 |
| Edition number | 23 |
| 100 1# - MAIN ENTRY--PERSONAL NAME | |
| Personal name | Yuan, Ye. |
| Relator term | author. |
| Relator code | aut |
| -- | http://id.loc.gov/vocabulary/relators/aut |
| 245 10 - TITLE STATEMENT | |
| Title | Latent Factor Analysis for High-dimensional and Sparse Matrices |
| Medium | [electronic resource] : |
| Remainder of title | A particle swarm optimization-based approach / |
| Statement of responsibility, etc | by Ye Yuan, Xin Luo. |
| 250 ## - EDITION STATEMENT | |
| Edition statement | 1st ed. 2022. |
| 264 #1 - | |
| -- | Singapore : |
| -- | Springer Nature Singapore : |
| -- | Imprint: Springer, |
| -- | 2022. |
| 300 ## - PHYSICAL DESCRIPTION | |
| Extent | VIII, 92 p. 1 illus. |
| Other physical details | online resource. |
| 336 ## - | |
| -- | text |
| -- | txt |
| -- | rdacontent |
| 337 ## - | |
| -- | computer |
| -- | c |
| -- | rdamedia |
| 338 ## - | |
| -- | online resource |
| -- | cr |
| -- | rdacarrier |
| 347 ## - | |
| -- | text file |
| -- | |
| -- | rda |
| 490 1# - SERIES STATEMENT | |
| Series statement | SpringerBriefs in Computer Science, |
| International Standard Serial Number | 2191-5776 |
| 505 0# - FORMATTED CONTENTS NOTE | |
| Formatted contents note | Chapter 1. Introduction -- Chapter 2. Learning rate-free Latent Factor Analysis via PSO -- Chapter 3. Learning Rate and Regularization Coefficient-free Latent Factor Analysis via PSO -- Chapter 4. Regularization and Momentum Coefficient-free Non-negative Latent Factor Analysis via PSO -- Chapter 5. Advanced Learning rate-free Latent Factor Analysis via P2SO -- Chapter 6. Conclusion and Discussion. |
| 520 ## - SUMMARY, ETC. | |
| Summary, etc | Latent factor analysis models are an effective type of machine learning model for addressing high-dimensional and sparse matrices, which are encountered in many big-data-related industrial applications. The performance of a latent factor analysis model relies heavily on appropriate hyper-parameters. However, most hyper-parameters are data-dependent, and using grid-search to tune these hyper-parameters is truly laborious and expensive in computational terms. Hence, how to achieve efficient hyper-parameter adaptation for latent factor analysis models has become a significant question. This is the first book to focus on how particle swarm optimization can be incorporated into latent factor analysis for efficient hyper-parameter adaptation, an approach that offers high scalability in real-world industrial applications. The book will help students, researchers and engineers fully understand the basic methodologies of hyper-parameter adaptation via particle swarm optimization in latent factor analysis models. Further, it will enable them to conduct extensive research and experiments on the real-world applications of the content discussed. |
| 650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM | |
| Topical term or geographic name as entry element | Artificial intelligence |
| General subdivision | Data processing. |
| 650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM | |
| Topical term or geographic name as entry element | Quantitative research. |
| 650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM | |
| Topical term or geographic name as entry element | Data mining. |
| 650 14 - SUBJECT ADDED ENTRY--TOPICAL TERM | |
| Topical term or geographic name as entry element | Data Science. |
| 650 24 - SUBJECT ADDED ENTRY--TOPICAL TERM | |
| Topical term or geographic name as entry element | Data Analysis and Big Data. |
| 650 24 - SUBJECT ADDED ENTRY--TOPICAL TERM | |
| Topical term or geographic name as entry element | Data Mining and Knowledge Discovery. |
| 700 1# - ADDED ENTRY--PERSONAL NAME | |
| Personal name | Luo, Xin. |
| Relator term | author. |
| Relator code | aut |
| -- | http://id.loc.gov/vocabulary/relators/aut |
| 710 2# - ADDED ENTRY--CORPORATE NAME | |
| Corporate name or jurisdiction name as entry element | SpringerLink (Online service) |
| 773 0# - HOST ITEM ENTRY | |
| Title | Springer Nature eBook |
| 776 08 - ADDITIONAL PHYSICAL FORM ENTRY | |
| Display text | Printed edition: |
| International Standard Book Number | 9789811967023 |
| 776 08 - ADDITIONAL PHYSICAL FORM ENTRY | |
| Display text | Printed edition: |
| International Standard Book Number | 9789811967047 |
| 830 #0 - SERIES ADDED ENTRY--UNIFORM TITLE | |
| Uniform title | SpringerBriefs in Computer Science, |
| -- | 2191-5776 |
| 856 40 - ELECTRONIC LOCATION AND ACCESS | |
| Uniform Resource Identifier | <a href="https://doi.org/10.1007/978-981-19-6703-0">https://doi.org/10.1007/978-981-19-6703-0</a> |
| 912 ## - | |
| -- | ZDB-2-SCS |
| 912 ## - | |
| -- | ZDB-2-SXCS |
| 942 ## - ADDED ENTRY ELEMENTS (KOHA) | |
| Koha item type | eBooks-CSE-Springer |
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