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008			110825s2011 gw | s |||| 0|eng d
020			_a9783642204296 _9978-3-642-20429-6
040			_cMX-MeUAM
050		4	_aTA1637-1638
050		4	_aTA1637-1638
082	0	4	_a006.6 _223
082	0	4	_a006.37 _223
100	1		_aChang, Edward Y. _eauthor.
245	1	0	_aFoundations of Large-Scale Multimedia Information Management and Retrieval _h[recurso electrónico] : _bMathematics of Perception / _cby Edward Y. Chang.
264		1	_aBerlin, Heidelberg : _bSpringer Berlin Heidelberg, _c2011.
300			_aXVIII, 291 p. _bonline resource.
336			_atext _btxt _2rdacontent
337			_acomputer _bc _2rdamedia
338			_aonline resource _bcr _2rdacarrier
347			_atext file _bPDF _2rda
505	0		_aPart I - Knowledge Representation and Semantic Analysis -- 1. Mathematics of Perception -- 2. Supervised Learning (based on tutorial DASFAA 2003) -- 3. Query Concept Learning (based on IEEE TMM 2005) -- 4. Feature Extraction -- 5. Feature Reduction (based on MM 04, ICME 05, IPAM) -- 6. Similarity (based on MMJ 2002, CIKM 04, ICML 05) -- Part II - Scalability Issues -- 7. Imbalanced Data Learning (based on TKDE 2005) -- 8. Semantics Fusion (based on MM 04, MM05, KDD 08) -- 9. Kernel Machines Speedup (based on SDM 05, KDD 06, NIPS 07) -- 10. Kernel Indexing (based on TKDE 06) -- 11. Put It All Together (based on SPIE 06).
520			_a"Foundations of Large-Scale Multimedia Information Management and Retrieval: Mathematics of Perception" covers knowledge representation and semantic analysis of multimedia data and scalability in signal extraction, data mining, and indexing. The book is divided into two parts: Part I - Knowledge Representation and Semantic Analysis focuses on the key components of mathematics of perception as it applies to data management and retrieval. These include feature selection/reduction, knowledge representation, semantic analysis, distance function formulation for measuring similarity, and multimodal fusion. Part II - Scalability Issues presents indexing and distributed methods for scaling up these components for high-dimensional data and Web-scale datasets. The book presents some real-world applications and remarks on future research and development directions.  The book is designed for researchers, graduate students, and practitioners in the fields of Computer Vision, Machine Learning, Large-scale Data Mining, Database, and Multimedia Information Retrieval. Dr. Edward Y. Chang was a professor at the Department of Electrical & Computer Engineering, University of California at Santa Barbara, before he joined Google as a research director in 2006. Dr. Chang received his M.S. degree in Computer Science and Ph.D degree in Electrical Engineering, both from Stanford University.
650		0	_aComputer science.
650		0	_aData mining.
650		0	_aMultimedia systems.
650		0	_aComputer vision.
650		0	_aEngineering.
650	1	4	_aComputer Science.
650	2	4	_aImage Processing and Computer Vision.
650	2	4	_aMachinery and Machine Elements.
650	2	4	_aData Mining and Knowledge Discovery.
650	2	4	_aMultimedia Information Systems.
710	2		_aSpringerLink (Online service)
773	0		_tSpringer eBooks
776	0	8	_iPrinted edition: _z9783642204289
856	4	0	_zLibro electrónico _uhttp://148.231.10.114:2048/login?url=http://link.springer.com/book/10.1007/978-3-642-20429-6
596			_a19
942			_cLIBRO_ELEC
999			_c203959 _d203959