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245	1	0	_aMachine Intelligence and Signal Processing _cedited by Richa Singh, Mayank Vatsa, Angshul Majumdar, Ajay Kumar
250			_a1st ed.
260			_aNew Delhi _bSpringer India _c2016
300			_a1 recurso en línea (X, 163 páginas) _b76 ilustraciones
336			_aTexto _btxt _2rdacontent
337			_aelectrónico _bc _2rdamedia
338			_arecurso electrónico _bcr _2rdacarrier
490	0		_aAdvances in Intelligent Systems and Computing _x2194-5357 _v390
505	0		_aChapter 1. Advancing Cross-spectral Iris Recognition Research using Bi-spectral Imaging -- Chapter 2. Fast 3D Salient Region Detection in Medical Images using GPUs -- Chapter 3. Recovering Partially Sampled EEG Signals using Learned Dictionaries -- Chapter 4. Greedy Algorithms for Non-linear Sparse Recovery -- Chapter 5. Improving Rating Predictions by Baseline Estimation and Single Pass Low-rank Approximation -- Chapter 6. Reducing Inter-scanner Variability in Multi-site fMRI Activations using Correction Functions: A Preliminary Study -- Chapter 7. Genetically Modified Logistic Regression with Radial Basis Function for Robust Software Effort Prediction -- Chapter 8. Missing Data Interpolation using Compressive Sensing: An Application for Sales Data Gathering -- Chapter 9. Retinal Vessel Classification based on Maximization of Squared-loss Mutual Information -- Chapter 10. Retinal Blood Vessel Extraction and Optic Disc Removal using Curvelet Transform and Morphological Operation -- Chapter 11. Adaptive Skin Color Model to Improve Video Face Detection -- Chapter 12. Automated Spam Detection in Short Text Messages -- Chapter 13. Domain Adaptation for Face Detection -- Chapter 14. Comparative Study of Pre-processing and Classification Methods in Character Recognition of Natural Scene Images.
520	3		_aThis book comprises chapters on key problems in machine learning and signal processing arenas. The contents of the book are a result of a 2014 Workshop on Machine Intelligence and Signal Processing held at the Indraprastha Institute of Information Technology. Traditionally, signal processing and machine learning were considered to be separate areas of research. However in recent times the two communities are getting closer. In a very abstract fashion, signal processing is the study of operator design. The contributions of signal processing had been to device operators for restoration, compression, etc. Applied Mathematicians were more interested in operator analysis. Nowadays signal processing research is gravitating towards operator learning � instead of designing operators based on heuristics (for example wavelets), the trend is to learn these operators (for example dictionary learning). And thus, the gap between signal processing and machine learning is fast converging. The 2014 Workshop on Machine Intelligence and Signal Processing was one of the few unique events that are focused on the convergence of the two fields. The book is comprised of chapters based on the top presentations at the workshop. This book has three chapters on various topics of biometrics � two are on face detection and one on iris recognition; all from top researchers in their field. There are four chapters on different biomedical signal / image processing problems. Two of these are on retinal vessel classification and extraction; one on biomedical signal acquisition and the fourth one on region detection. There are three chapters on data analysis � a topic gaining immense popularity in industry and academia. One of these shows a novel use of compressed sensing in missing sales data interpolation. Another chapter is on spam detection and the third one is on simple one-shot movie rating prediction. Four other chapters cover various cutting edge miscellaneous topics on character recognition, software effort prediction, speech recognition and non-linear sparse recovery. The contents of this book will prove useful to researchers, professionals and students in the domains of machine learning and signal processing.
650	0	7	_aAprendizaje automático _2embne _9166090
650	0	7	_9150608 _aProceso de señales _2embne
700	1		_aSingh, Richa. _eeditor literario _9100514 _0Local
700	1		_aVatsa, Mayank. _eeditor literario _9100515 _0Local
700	1		_aMajumdar, Angshul. _eeditor literario _9100516 _0Local
700	1		_aKumar, Ajay _eeditor literario _0Local _9100517
710	2		_aSpringerLink (Online service) _0Local _9106996
856	4	0	_uhttps://go.openathens.net/redirector/universidadeuropea.es?url=https://link.springer.com/book/10.1007/978-81-322-2625-3 _zAcceso a este recurso digital (usuarios Universidad Europea de Madrid)
901			_ai9788132226253
907			_a.b12959224 _b10-10-17 _c21-11-16
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945			_aQ325.5 M334 2016 EB _g1 _ieBOOK _j0 _lmae _o- _pEUR0.00 _q- _r- _sb _t15 _u0 _v0 _w0 _x0 _y.i11600652 _z06-04-17
988			_aEBOOK, asignarmaterias , EBSPRINGER
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