From Unimodal to Multimodal Machine Learning (Record no. 88034)
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| fixed length control field | 03936nam a22005055i 4500 |
| 001 - CONTROL NUMBER | |
| control field | 978-3-031-57016-2 |
| 005 - DATE AND TIME OF LATEST TRANSACTION | |
| control field | 20240730172125.0 |
| 008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION | |
| fixed length control field | 240521s2024 sz | s |||| 0|eng d |
| 020 ## - INTERNATIONAL STANDARD BOOK NUMBER | |
| ISBN | 9783031570162 |
| -- | 978-3-031-57016-2 |
| 082 04 - CLASSIFICATION NUMBER | |
| Call Number | 006.31 |
| 100 1# - AUTHOR NAME | |
| Author | Škrlj, Blaž. |
| 245 10 - TITLE STATEMENT | |
| Title | From Unimodal to Multimodal Machine Learning |
| Sub Title | An Overview / |
| 250 ## - EDITION STATEMENT | |
| Edition statement | 1st ed. 2024. |
| 300 ## - PHYSICAL DESCRIPTION | |
| Number of Pages | XIII, 70 p. 15 illus., 14 illus. in color. |
| 490 1# - SERIES STATEMENT | |
| Series statement | SpringerBriefs in Computer Science, |
| 505 0# - FORMATTED CONTENTS NOTE | |
| Remark 2 | Part .I. Introduction -- Chapter.1.A brief overview of machine learning -- Chapter.2.Data modalities and representation learning -- Part II Unimodal machine learning -- Chapter.3.Learning from text -- Chapter.4.Graph-based methods -- Chapter.5 Computer vision -- Part. III. Multimodal machine learning -- Chapter.6.Multimodal learning -- Part. IV.A look forward -- Chapter.7.Future prospects. |
| 520 ## - SUMMARY, ETC. | |
| Summary, etc | With the increasing amount of various data types, machine learning methods capable of leveraging diverse sources of information have become highly relevant. Deep learning-based approaches have made significant progress in learning from texts and images in recent years. These methods enable simultaneous learning from different types of representations (embeddings). Substantial advancements have also been made in joint learning from different types of spaces. Additionally, other modalities such as sound, physical signals from the environment, and time series-based data have been recently explored. Multimodal machine learning, which involves processing and learning from data across multiple modalities, has opened up new possibilities in a wide range of applications, including speech recognition, natural language processing, and image recognition. From Unimodal to Multimodal Machine Learning: An Overview gradually introduces the concept of multimodal machine learning, providing readers with the necessary background to understand this type of learning and its implications. Key methods representative of different modalities are described in more detail, aiming to offer an understanding of the peculiarities of various types of data and how multimodal approaches tend to address them (although not yet in some cases). The book examines the implications of multimodal learning in other domains and presents alternative approaches that offer computationally simpler yet still applicable solutions. The final part of the book focuses on intriguing open research problems, making it useful for practitioners who wish to better understand the limitations of existing methods and explore potential research avenues to overcome them. |
| 650 #0 - SUBJECT ADDED ENTRY--SUBJECT 1 | |
| General subdivision | Data processing. |
| 856 40 - ELECTRONIC LOCATION AND ACCESS | |
| Uniform Resource Identifier | https://doi.org/10.1007/978-3-031-57016-2 |
| 942 ## - ADDED ENTRY ELEMENTS (KOHA) | |
| Koha item type | eBooks |
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| -- | Cham : |
| -- | Springer Nature Switzerland : |
| -- | Imprint: Springer, |
| -- | 2024. |
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| -- | computer |
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| -- | online resource |
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| -- | text file |
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| -- | rda |
| 650 #0 - SUBJECT ADDED ENTRY--SUBJECT 1 | |
| -- | Machine learning. |
| 650 #0 - SUBJECT ADDED ENTRY--SUBJECT 1 | |
| -- | Artificial intelligence |
| 650 14 - SUBJECT ADDED ENTRY--SUBJECT 1 | |
| -- | Machine Learning. |
| 650 24 - SUBJECT ADDED ENTRY--SUBJECT 1 | |
| -- | Data Science. |
| 830 #0 - SERIES ADDED ENTRY--UNIFORM TITLE | |
| -- | 2191-5776 |
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