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008			161005s2016 gw | s |||| 0|eng d
020			_a9783319450261
040			_aES-MaUEC
050		4	_aQA671 _b.A446 2016
082	0	4	_a006.4
245	0	0	_aAlgorithmic Advances in Riemannian Geometry and Applications : _bfor Machine Learning, Computer Vision, Statistics, and Optimization _cedited by Hà Quang Minh, Vittorio Murino
260			_aCham _bSpringer International Publishing _c2016
300			_a1 recurso en línea (XIV, 208 páginas) _b55 ilustraciones, 51 ilustraciones en color
336			_aTexto _btxt _2rdacontent
337			_aelectrónico _bc _2rdamedia
338			_arecurso electrónico _bcr _2rdacarrier
490	0		_aAdvances in Computer Vision and Pattern Recognition _x2191-6586
505	0		_aIntroduction -- Bayesian Statistical Shape Analysis on the Manifold of Diffeomorphisms -- Sampling Constrained Probability Distributions using Spherical Augmentation -- Geometric Optimization in Machine Learning -- Positive Definite Matrices: Data Representation and Applications to Computer Vision -- From Covariance Matrices to Covariance Operators: Data Representation from Finite to Infinite-Dimensional Settings -- Dictionary Learning on Grassmann Manifolds -- Regression on Lie Groups and its Application to Affine Motion Tracking -- An Elastic Riemannian Framework for Shape Analysis of Curves and Tree-Like Structures.
520	3		_aThis book presents a selection of the most recent algorithmic advances in Riemannian geometry in the context of machine learning, statistics, optimization, computer vision, and related fields. The unifying theme of the different chapters in the book is the exploitation of the geometry of data using the mathematical machinery of Riemannian geometry. As demonstrated by all the chapters in the book, when the data is intrinsically non-Euclidean, the utilization of this geometrical information can lead to better algorithms that can capture more accurately the structures inherent in the data, leading ultimately to better empirical performance. This book is not intended to be an encyclopedic compilation of the applications of Riemannian geometry. Instead, it focuses on several important research directions that are currently actively pursued by researchers in the field. These include statistical modeling and analysis on manifolds,optimization on manifolds, Riemannian manifolds and kernel methods, and dictionary learning and sparse coding on manifolds. Examples of applications include novel algorithms for Monte Carlo sampling and Gaussian Mixture Model fitting, 3D brain image analysis,image classification, action recognition, and motion tracking.
650		7	_aEstadística matemática _0comprobar BNE19900966258 _2embne _9138936
650		7	_aInteligencia artificial _0comprobar BNE19900997218 _2embne _9413115
700			_aMinh, Hà Quang _eeditor literario _0Local _9100006
700			_aMurino, Vittorio _eeditor literario _0Local _9100007
710	2		_aSpringerLink (Online service) _0Local _9106996
856	4	0	_uhttps://go.openathens.net/redirector/universidadeuropea.es?url=https://link.springer.com/book/10.1007/978-3-319-45026-1 _zAcceso a este recurso digital (usuarios Universidad Europea de Madrid)
901			_ai9783319450261
907			_a.b12956272 _b10-10-17 _c21-11-16
942			_2lcc _cLE
945			_aQA671 .A446 2016 EB _g1 _ieBOOK _j0 _lmae _o- _pEUR0.00 _q- _r- _sb _t15 _u0 _v0 _w0 _x0 _y.i11598207 _z06-04-17
988			_aEBOOK, asignarmaterias , EBSPRINGER
998			_am _a_alco _a_vill _b - - _cm _dz _e- _feng _ggw _h0