000			03872nam a22004095i 4500
001			86498
003			ES-MaUEC
005			20230207040601.0
007			cr nn 008mamaa
008			160219s2016 gw | s |||| 0|eng d
020			_a9783662488386
040			_aES-MaUEC
050		4	_aQ325.5 _bM334 2016
082	0	4	_a006.3
245	1	0	_aMachine Learning for Cyber Physical Systems : _bSelected papers from the International Conference ML4CPS 2015 _cedited by Oliver Niggemann, Jürgen Beyerer
250			_a1st ed.
260			_aBerlin, Heidelberg _bSpringer Berlin Heidelberg Vieweg _c2016
300			_a1 recurso en línea (VI, 121 páginas) _b12 ilustraciones en color
336			_aTexto _btxt _2rdacontent
337			_aelectrónico _bc _2rdamedia
338			_arecurso electrónico _bcr _2rdacarrier
490	0		_aTechnologien für die intelligente Automation, Technologies for Intelligent Automation
505	0		_aDevelopment of a Cyber-Physical System based on selective dynamic Gaussian naive Bayes model for a self-predict laser surface heat treatment process control -- Evidence Grid Based Information Fusion for Semantic Classifiers in Dynamic Sensor Networks -- Forecasting Cellular Connectivity for Cyber- Physical Systems: A Machine Learning Approach -- Towards Optimized Machine Operations by Cloud Integrated Condition Estimation -- Prognostics Health� Management System based on Hybrid Model to Predict Failures of a Planetary Gear Transmission -- Evaluation of Model-Based Condition Monitoring�Systems in Industrial Application Cases -- Towards a novel learning assistant for networked automation systems -- Effcient Image Processing System for an�Industrial Machine Learning Task -- Efficient engineering in special purpose machinery through automated control code synthesis based on a functional�categorisation -- Geo-Distributed Analytics for the Internet of Things -- Imple mentation and Comparison of Cluster-Based PSO Extensions in Hybrid�Settings with Efficient Approximation -- Machine-specifc Approach for Automatic Classifcation of Cutting Process Efficiency -- Meta-analysis of Maintenance�Knowledge Assets Towards Predictive Cost Controlling of Cyber Physical Production Systems -- Towards Autonomously Navigating and Cooperating�Vehicles in Cyber-Physical Production Systems.
520	3		_aThe work presents new approaches to Machine Learning for Cyber Physical Systems, experiences and visions. It contains some selected papers from the international Conference ML4CPS � Machine Learning for Cyber Physical Systems, which was held in Lemgo, October 1-2, 2015. Cyber Physical Systems are characterized by their ability to adapt and to learn: They analyze their environment and, based on observations, they learn patterns, correlations and predictive models. Typical applications are condition monitoring, predictive maintenance, image processing and diagnosis. Machine Learning is the key technology for these developments.
710	2		_aSpringerLink (Online service) _0Local _9106996
942			_2lcc _cLE
988			_aEBOOK, asignarmaterias , EBSPRINGER
650		7	_aGestión del conocimiento _9157801 _0comprobar BNE20003984217 _2embne
650		7	_aAprendizaje automático _0(OCoLC)1004795 _2embne _0 _9166090
700	1		_aNiggemann, Oliver. _eeditor literario _9100247 _0Local
700	1		_aBeyerer, Jürgen. _eeditor literario _9100176 _0Local
856	4	0	_uhttps://go.openathens.net/redirector/universidadeuropea.es?url=https://link.springer.com/book/10.1007/978-3-662-48838-6 _zAcceso a este recurso digital (usuarios Universidad Europea de Madrid)
901			_ai9783662488386
907			_a.b12957768 _b10-10-17 _c21-11-16
998			_am _a_alco _a_vill _b - - _cm _dz _e- _feng _ggw _h0
945			_aQ325.5 M334 2016 EB _g1 _ieBOOK _j0 _lmae _o- _pEUR0.00 _q- _r- _sb _t15 _u0 _v0 _w0 _x0 _y.i11599522 _z06-04-17
999			_c86498 _d86498 _x1