000			03535nam a22005895i 4500
001			978-3-319-45026-1
003			DE-He213
005			20200421112045.0
007			cr nn 008mamaa
008			161005s2016 gw | s |||| 0|eng d
020			_a9783319450261 _9978-3-319-45026-1
024	7		_a10.1007/978-3-319-45026-1 _2doi
050		4	_aQ337.5
050		4	_aTK7882.P3
072		7	_aUYQP _2bicssc
072		7	_aCOM016000 _2bisacsh
082	0	4	_a006.4 _223
245	1	0	_aAlgorithmic Advances in Riemannian Geometry and Applications _h[electronic resource] : _bFor Machine Learning, Computer Vision, Statistics, and Optimization / _cedited by H�a Quang Minh, Vittorio Murino.
264		1	_aCham : _bSpringer International Publishing : _bImprint: Springer, _c2016.
300			_aXIV, 208 p. 55 illus., 51 illus. in color. _bonline resource.
336			_atext _btxt _2rdacontent
337			_acomputer _bc _2rdamedia
338			_aonline resource _bcr _2rdacarrier
347			_atext file _bPDF _2rda
490	1		_aAdvances in Computer Vision and Pattern Recognition, _x2191-6586
520			_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		0	_aComputer science.
650		0	_aMathematical statistics.
650		0	_aArtificial intelligence.
650		0	_aPattern recognition.
650		0	_aComputer science _xMathematics.
650		0	_aComputer mathematics.
650		0	_aStatistics.
650		0	_aComputational intelligence.
650	1	4	_aComputer Science.
650	2	4	_aPattern Recognition.
650	2	4	_aComputational Intelligence.
650	2	4	_aStatistics and Computing/Statistics Programs.
650	2	4	_aMathematical Applications in Computer Science.
650	2	4	_aArtificial Intelligence (incl. Robotics).
650	2	4	_aProbability and Statistics in Computer Science.
700	1		_aMinh, H�a Quang. _eeditor.
700	1		_aMurino, Vittorio. _eeditor.
710	2		_aSpringerLink (Online service)
773	0		_tSpringer eBooks
776	0	8	_iPrinted edition: _z9783319450254
830		0	_aAdvances in Computer Vision and Pattern Recognition, _x2191-6586
856	4	0	_uhttp://dx.doi.org/10.1007/978-3-319-45026-1
912			_aZDB-2-SCS
942			_cEBK
999			_c56886 _d56886