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_223
100 1 _aŠkrlj, Blaž.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
_9101915
245 1 0 _aFrom Unimodal to Multimodal Machine Learning
_h[electronic resource] :
_bAn Overview /
_cby Blaž Škrlj.
250 _a1st ed. 2024.
264 1 _aCham :
_bSpringer Nature Switzerland :
_bImprint: Springer,
_c2024.
300 _aXIII, 70 p. 15 illus., 14 illus. in color.
_bonline resource.
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
347 _atext file
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490 1 _aSpringerBriefs in Computer Science,
_x2191-5776
505 0 _aPart .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 _aWith 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 _aMachine learning.
_91831
650 0 _aArtificial intelligence
_xData processing.
_921787
650 1 4 _aMachine Learning.
_91831
650 2 4 _aData Science.
_934092
710 2 _aSpringerLink (Online service)
_9101918
773 0 _tSpringer Nature eBook
776 0 8 _iPrinted edition:
_z9783031570155
776 0 8 _iPrinted edition:
_z9783031570179
830 0 _aSpringerBriefs in Computer Science,
_x2191-5776
_9101919
856 4 0 _uhttps://doi.org/10.1007/978-3-031-57016-2
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