| 书目名称 | Machine Learning for Audio, Image and Video Analysis | | 副标题 | Theory and Applicati | | 编辑 | Francesco Camastra,Alessandro Vinciarelli | | 视频video | | | 概述 | Presents techniques for extracting features from audio recordings, images and videos.Provides the mathematical background required to use the techniques described.Covers the most important machine lea | | 丛书名称 | Advanced Information and Knowledge Processing | | 图书封面 |  | | 描述 | .This second edition focuses on audio, image and video data, the three main types of input that machines deal with when interacting with the real world. A set of appendices provides the reader with self-contained introductions to the mathematical background necessary to read the book..Divided into three main parts, .From Perception to Computation. introduces methodologies aimed at representing the data in forms suitable for computer processing, especially when it comes to audio and images. Whilst the second part, .Machine Learning. includes an extensive overview of statistical techniques aimed at addressing three main problems, namely classification (automatically assigning a data sample to one of the classes belonging to a predefined set), clustering (automatically grouping data samples according to the similarity of their properties) and sequence analysis (automatically mapping a sequence of observations into a sequence of human-understandable symbols). The third part.Applications. shows how the abstract problems defined in the second part underlie technologies capable to perform complex tasks such as the recognition of hand gestures or the transcription of handwritten data..Mach | | 出版日期 | Textbook 2015Latest edition | | 关键词 | Cluster Analysis; Image and Video Data; Machine Learning; Sequence Analysis; Signal Processing | | 版次 | 2 | | doi | https://doi.org/10.1007/978-1-4471-6735-8 | | isbn_softcover | 978-1-4471-6840-9 | | isbn_ebook | 978-1-4471-6735-8Series ISSN 1610-3947 Series E-ISSN 2197-8441 | | issn_series | 1610-3947 | | copyright | Springer-Verlag London 2015 |
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发表于 2025-3-21 16:27:11
