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020 _a9781447167358
_z978-1-4471-6735-8
024 7 _a10.1007/978-1-4471-6735-8
_2doi
050 4 _aQ337.5
050 4 _aTK7882.P3
072 7 _aUYQP
_2bicssc
072 7 _aCOM016000
_2bisacsh
072 7 _aUYQP
_2thema006.4
_223
100 1 _aCamastra, Francesco.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
245 1 0 _aMachine Learning for Audio, Image and Video Analysis :
_bTheory and Applications /
_cby Francesco Camastra, Alessandro Vinciarelli.
250 _a2nd ed. 2015.
264 1 _aLondon :
_bSpringer London :
_bImprint: Springer,
_c2015.
300 _a1 online resource
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
347 _atext file
_bPDF
_2rda
490 0 _aAdvanced Information and Knowledge Processing,
_x1610-3947
505 0 _aIntroduction -- 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 _aThis 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 _aOptical pattern recognition.
650 0 _aComputer vision.
650 0 _aMultimedia systems.
650 1 4 _aPattern Recognition.
_0http://scigraph.springernature.com/things/product-market-codes/I2203X
650 2 4 _aImage Processing and Computer Vision.
_0http://scigraph.springernature.com/things/product-market-codes/I22021
650 2 4 _aMultimedia Information Systems.
_0http://scigraph.springernature.com/things/product-market-codes/I18059
700 1 _aVinciarelli, Alessandro.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
710 2 _aSpringerLink (Online service)
856 4 0 _uhttps://doi.org/10.1007/978-1-4471-6735-8
_3Springer eBooks
_zOnline access link to the resource
912 _aZDB-2-SCS
999 _c200433730
_d51942
942 _2lcc
_cEBK
041 _aeng