000			04053nam a22003855i 4500
001			292127
003			MX-SnUAN
005			20160429154939.0
007			cr nn 008mamaa
008			150903s2013 gw | o |||| 0|eng d
020			_a9783319013213 _99783319013213
024	7		_a10.1007/9783319013213 _2doi
035			_avtls000345964
039		9	_a201509030910 _bVLOAD _c201405050327 _dVLOAD _y201402070845 _zstaff
040			_aMX-SnUAN _bspa _cMX-SnUAN _erda
050		4	_aQA71-90
100	1		_aPaprotny, Alexander. _eautor _9323707
245	1	0	_aRealtime Data Mining : _bSelf-Learning Techniques for Recommendation Engines / _cby Alexander Paprotny, Michael Thess.
264		1	_aCham : _bSpringer International Publishing : _bImprint: Birkhäuser, _c2013.
300			_axxiii, 313 páginas 100 ilustraciones, 12 ilustraciones en color. _brecurso en línea.
336			_atexto _btxt _2rdacontent
337			_acomputadora _bc _2rdamedia
338			_arecurso en línea _bcr _2rdacarrier
347			_aarchivo de texto _bPDF _2rda
490	0		_aApplied and Numerical Harmonic Analysis, _x2296-5009
500			_aSpringer eBooks
505	0		_a1 Brave New Realtime World – Introduction -- 2 Strange Recommendations? – On The Weaknesses Of Current Recommendation Engines -- 3 Changing Not Just Analyzing – Control Theory And Reinforcement Learning -- 4 Recommendations As A Game – Reinforcement Learning For Recommendation Engines -- 5 How Engines Learn To Generate Recommendations – Adaptive Learning Algorithms -- 6 Up The Down Staircase – Hierarchical Reinforcement Learning -- 7 Breaking Dimensions – Adaptive Scoring With Sparse Grids -- 8 Decomposition In Transition - Adaptive Matrix Factorization -- 9 Decomposition In Transition Ii - Adaptive Tensor Factorization -- 10 The Big Picture – Towards A Synthesis Of Rl And Adaptive Tensor Factorization -- 11 What Cannot Be Measured Cannot Be Controlled - Gauging Success With A/B Tests -- 12 Building A Recommendation Engine – The Xelopes Library -- 13 Last Words – Conclusion -- References -- Summary Of Notation.
520			_aDescribing novel mathematical concepts for recommendation engines, Realtime Data Mining: Self-Learning Techniques for Recommendation Engines features a sound mathematical framework unifying approaches based on control and learning theories, tensor factorization, and hierarchical methods. Furthermore, it presents promising results of numerous experiments on real-world data.  The area of realtime data mining is currently developing at an exceptionally dynamic pace, and realtime data mining systems are the counterpart of today's “classic” data mining systems. Whereas the latter learn from historical data and then use it to deduce necessary actions, realtime analytics systems learn and act continuously and autonomously. In the vanguard of these new analytics systems are recommendation engines. They are principally found on the Internet, where all information is available in realtime and an immediate feedback is guaranteed.   This monograph appeals to computer scientists and specialists in machine learning, especially from the area of recommender systems, because it conveys a new way of realtime thinking by considering recommendation tasks as control-theoretic problems. Realtime Data Mining: Self-Learning Techniques for Recommendation Engines will also interest application-oriented mathematicians because it consistently combines some of the most promising mathematical areas, namely control theory, multilevel approximation, and tensor factorization.
590			_aPara consulta fuera de la UANL se requiere clave de acceso remoto.
700	1		_aThess, Michael. _eautor _9323708
710	2		_aSpringerLink (Servicio en línea) _9299170
776	0	8	_iEdición impresa: _z9783319013206
856	4	0	_uhttp://remoto.dgb.uanl.mx/login?url=http://dx.doi.org/10.1007/978-3-319-01321-3 _zConectar a Springer E-Books (Para consulta externa se requiere previa autentificación en Biblioteca Digital UANL)
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