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Machine learning for audio, image and video analysis = theory and applications /

Record Type:	Language materials, printed : Monograph/item
Title/Author:	Machine learning for audio, image and video analysis/ by Francesco Camastra, Alessandro Vinciarelli.
Reminder of title:	theory and applications /
Author:	Camastra, Francesco.
other author:	Vinciarelli, Alessandro.
Published:	London :Springer London : : 2015.,
Description:	xvi, 561 p. :ill., digital ; : 24 cm.;
Contained By:	Springer eBooks
Subject:	Machine learning. -
Online resource:	http://dx.doi.org/10.1007/978-1-4471-6735-8
ISBN:	9781447167358 (electronic bk.)

Machine learning for audio, image and video analysis = theory and applications /
Camastra, Francesco.

Machine learning for audio, image and video analysistheory and applications /[electronic resource] :by Francesco Camastra, Alessandro Vinciarelli. - 2nd ed. - London :Springer London :2015. - xvi, 561 p. :ill., digital ;24 cm. - Advanced information and knowledge processing,1610-3947. - Advanced information and knowledge processing..

Introduction -- Part I: From Perception to Computation -- Audio Acquisition, Representation and Storage -- Image and Video Acquisition, Representation and Storage -- Part II: Machine Learning -- Machine Learning -- Bayesian Theory of Decision -- Clustering Methods -- Foundations of Statistical Learning and Model Selection -- Supervised Neural Networks and Ensemble Methods -- Kernel Methods -- Markovian Models for Sequential Data -- Feature Extraction Methods and Manifold Learning Methods -- Part III: Applications -- Speech and Handwriting Recognition -- Speech and Handwriting Recognition -- Video Segmentation and Keyframe Extraction -- Real-Time Hand Pose Recognition -- Automatic Personality Perception -- Part IV: Appendices -- Appendix A: Statistics -- Appendix B: Signal Processing -- Appendix C: Matrix Algebra -- Appendix D: Mathematical Foundations of Kernel Methods -- Index.

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. Machine Learning for Audio, Image and Video Analysis is suitable for students to acquire a solid background in machine learning as well as for practitioners to deepen their knowledge of the state-of-the-art. All application chapters are based on publicly available data and free software packages, thus allowing readers to replicate the experiments.

ISBN: 9781447167358 (electronic bk.)

Standard No.: 10.1007/978-1-4471-6735-8doiSubjects--Topical Terms:

561253
Machine learning.

LC Class. No.: Q325.5

Dewey Class. No.: 006.31

Machine learning for audio, image and video analysis = theory and applications /
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505 0 $a Introduction -- Part I: From Perception to Computation -- Audio Acquisition, Representation and Storage -- Image and Video Acquisition, Representation and Storage -- Part II: Machine Learning -- Machine Learning -- Bayesian Theory of Decision -- Clustering Methods -- Foundations of Statistical Learning and Model Selection -- Supervised Neural Networks and Ensemble Methods -- Kernel Methods -- Markovian Models for Sequential Data -- Feature Extraction Methods and Manifold Learning Methods -- Part III: Applications -- Speech and Handwriting Recognition -- Speech and Handwriting Recognition -- Video Segmentation and Keyframe Extraction -- Real-Time Hand Pose Recognition -- Automatic Personality Perception -- Part IV: Appendices -- Appendix A: Statistics -- Appendix B: Signal Processing -- Appendix C: Matrix Algebra -- Appendix D: Mathematical Foundations of Kernel Methods -- Index.
520 $a 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. Machine Learning for Audio, Image and Video Analysis is suitable for students to acquire a solid background in machine learning as well as for practitioners to deepen their knowledge of the state-of-the-art. All application chapters are based on publicly available data and free software packages, thus allowing readers to replicate the experiments.
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