000 03076nam a22005295i 4500
001 978-3-642-30752-2
003 DE-He213
005 20200420220217.0
007 cr nn 008mamaa
008 120720s2013 gw | s |||| 0|eng d
020 _a9783642307522
_9978-3-642-30752-2
024 7 _a10.1007/978-3-642-30752-2
_2doi
050 4 _aTK5102.9
050 4 _aTA1637-1638
050 4 _aTK7882.S65
072 7 _aTTBM
_2bicssc
072 7 _aUYS
_2bicssc
072 7 _aTEC008000
_2bisacsh
072 7 _aCOM073000
_2bisacsh
082 0 4 _a621.382
_223
100 1 _aSalazar, Addisson.
_eauthor.
245 1 0 _aOn Statistical Pattern Recognition in Independent Component Analysis Mixture Modelling
_h[electronic resource] /
_cby Addisson Salazar.
264 1 _aBerlin, Heidelberg :
_bSpringer Berlin Heidelberg :
_bImprint: Springer,
_c2013.
300 _aXXII, 186 p.
_bonline resource.
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
347 _atext file
_bPDF
_2rda
490 1 _aSpringer Theses, Recognizing Outstanding Ph.D. Research,
_x2190-5053 ;
_v4
505 0 _aIntroduction -- ICA and ICAMM Methods -- Learning Mixtures of Independent Component Analysers -- Hierarchical Clustering from ICA Mixtures -- Application of ICAMM to Impact-Echo Testing -- Cultural Heritage Applications: Archaeological Ceramics and Building Restoration -- Other Applications: Sequential Dependence Modelling and Data Mining -- Conclusions.
520 _aA natural evolution of statistical signal processing, in connection with the progressive increase in computational power, has been exploiting higher-order information. Thus, high-order spectral analysis and nonlinear adaptive filtering have received the attention of many researchers. One of the most successful techniques for non-linear processing of data with complex non-Gaussian distributions is the independent component analysis mixture modelling (ICAMM). This thesis defines a novel formalism for pattern recognition and classification based on ICAMM, which unifies a certain number of pattern recognition tasks allowing generalization. The versatile and powerful framework developed in this work can deal with data obtained from quite different areas, such as image processing, impact-echo testing, cultural heritage, hypnograms analysis, web-mining and might therefore be employed to solve many different real-world problems.
650 0 _aEngineering.
650 0 _aPattern recognition.
650 0 _aComplexity, Computational.
650 1 4 _aEngineering.
650 2 4 _aSignal, Image and Speech Processing.
650 2 4 _aPattern Recognition.
650 2 4 _aComplexity.
710 2 _aSpringerLink (Online service)
773 0 _tSpringer eBooks
776 0 8 _iPrinted edition:
_z9783642307515
830 0 _aSpringer Theses, Recognizing Outstanding Ph.D. Research,
_x2190-5053 ;
_v4
856 4 0 _uhttp://dx.doi.org/10.1007/978-3-642-30752-2
912 _aZDB-2-ENG
942 _cEBK
999 _c51652
_d51652