000			03084nam a22003855i 4500
999			_c103470 _d103470 _x1
001			103470
003			DE-He213
005			20230102113131.0
006			a||||fo|||| 00| 0
007			cr nn 008mamaa
008			150214s2015 si | s |||| 0|eng d
020			_a9789812874115
024	7		_a10.1007/978-981-287-411-5 _2doi
040			_bspa _dES-MaUEC
050		4	_aQ325.5 _b2015 EB
100	1		_aDe Silva, Anthony Mihirana. _eautor. _4aut _4http://id.loc.gov/vocabulary/relators/aut
245	1	0	_aGrammar-Based Feature Generation for Time-Series Prediction _cby Anthony Mihirana De Silva, Philip H. W. Leong.
264		1	_aSingapore _bSpringer International Publishing _c2015
300			_a1 recurso en línea (XI, 99 páginas 28 ilustraciones)
336			_2rdacontent _aTexto (visual) _btxt
337			_2rdamedia _aelectrónico _bc
338			_2rdacarrier _arecurso electrónico _bcr
490	0		_aSpringerBriefs in Computational Intelligence, _x2625-3704
490	0		_aEngineering (Springer-11647)
505	0		_aIntroduction -- Feature Selection -- Grammatical Evolution -- Grammar Based Feature Generation -- Application of Grammar Framework to Time-series Prediction -- Case Studies -- Conclusion.
520	3		_aThis book proposes a novel approach for time-series prediction using machine learning techniques with automatic feature generation. Application of machine learning techniques to predict time-series continues to attract considerable attention due to the difficulty of the prediction problems compounded by the non-linear and non-stationary nature of the real world time-series. The performance of machine learning techniques, among other things, depends on suitable engineering of features. This book proposes a systematic way for generating suitable features using context-free grammar. A number of feature selection criteria are investigated and a hybrid feature generation and selection algorithm using grammatical evolution is proposed. The book contains graphical illustrations to explain the feature generation process. The proposed approaches are demonstrated by predicting the closing price of major stock market indices, peak electricity load and net hourly foreign exchange client trade volume. The proposed method can be applied to a wide range of machine learning architectures and applications to represent complex feature dependencies explicitly when machine learning cannot achieve this by itself. Industrial applications can use the proposed technique to improve their predictions.
988			_aEBSPRINGER_2018
650		7	_aAprendizaje automático _2embne _9166090
700	1		_aLeong, Philip H. W. _eautor. _4aut _4http://id.loc.gov/vocabulary/relators/aut _1http://viaf.org/viaf/209706199/
776	0	8	_iEdición impresa: _z9789812874122
776	0	8	_iEdición impresa: _z9789812874108
856	4	0	_uhttps://go.openathens.net/redirector/universidadeuropea.es?url=https://doi.org/10.1007/978-981-287-411-5 _zAcceso a este recurso digital (usuarios Universidad Europea de Madrid)
942			_2lcc _cLE
998			_b03/2019 _dz _eIG _zSI