國立高雄大學圖資館 |

語系: 繁體中文

說明(常見問題)

圖資館首頁

登入

回首頁

切換: 標籤 | MARC模式 | ISBD

Machine learning for audio, image an...

Camastra, Francesco.

Machine learning for audio, image and video analysistheory and applications /

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Machine learning for audio, image and video analysisby Francesco Camastra, Alessandro Vinciarelli.
其他題名:	theory and applications /
作者:	Camastra, Francesco.
其他作者:	Vinciarelli, Alessandro.
出版者:	London :Springer London :2015.
面頁冊數:	xvi, 561 p. :ill., digital ;24 cm.
Contained By:	Springer eBooks
標題:	Machine learning.
電子資源:	http://dx.doi.org/10.1007/978-1-4471-6735-8
ISBN:	9781447167358 (electronic bk.)

Machine learning for audio, image and video analysistheory 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:

188639
Machine learning.

LC Class. No.: Q325.5

Dewey Class. No.: 006.31

Machine learning for audio, image and video analysistheory and applications /
LDR:03527nmm a2200337 a 4500 001 472543
003 DE-He213
005 20160222154049.0
006 m d
007 cr nn 008maaau
008 160316s2015 enk s 0 eng d
020 $a 9781447167358 (electronic bk.)
020 $a 9781447167341 (paper)
024 7 $a 10.1007/978-1-4471-6735-8 $2 doi
035 $a 978-1-4471-6735-8
040 $a GP $c GP
041 0 $a eng
050 4 $a Q325.5
072 7 $a UYQP $2 bicssc
072 7 $a COM016000 $2 bisacsh
082 0 4 $a 006.31 $2 23
090 $a Q325.5 $b .C173 2015
100 1 $a Camastra, Francesco. $3 275387
245 1 0 $a Machine learning for audio, image and video analysis $h [electronic resource] : $b theory and applications / $c by Francesco Camastra, Alessandro Vinciarelli.
250 $a 2nd ed.
260 $a London : $b Springer London : $b Imprint: Springer, $c 2015.
300 $a xvi, 561 p. : $b ill., digital ; $c 24 cm.
490 1 $a Advanced information and knowledge processing, $x 1610-3947
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.
650 0 $a Machine learning. $3 188639
650 0 $a Pattern recognition systems. $3 183725
650 0 $a Image analysis. $3 190419
650 1 4 $a Computer Science. $3 212513
650 2 4 $a Pattern Recognition. $3 273706
650 2 4 $a Image Processing and Computer Vision. $3 274051
650 2 4 $a Multimedia Information Systems. $3 274489
700 1 $a Vinciarelli, Alessandro. $3 275386
710 2 $a SpringerLink (Online service) $3 273601
773 0 $t Springer eBooks
830 0 $a Advanced information and knowledge processing. $3 560385
856 4 0 $u http://dx.doi.org/10.1007/978-1-4471-6735-8
950 $a Computer Science (Springer-11645)