Ensemble Gaussian mixture models for probability density estimation

Abstract Estimation of probability density functions (PDF) is a fundamental concept in statistics. This paper proposes an ensemble learning approach for density estimation using Gaussian mixture models (GMM). Ensemble learning is closely related to model averaging: While the standard model selection...
Ausführliche Beschreibung

Gespeichert in:

Autor*in:	Glodek, Michael [verfasserIn] Schels, Martin Schwenker, Friedhelm

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2012

Schlagwörter:	Ensemble learning Density estimation Finite mixture models

Anmerkung:	© Springer-Verlag Berlin Heidelberg 2012

Übergeordnetes Werk:	Enthalten in: Computational statistics - Berlin : Springer, 1999, 28(2012), 1 vom: 26. Okt., Seite 127-138
Übergeordnetes Werk:	volume:28 ; year:2012 ; number:1 ; day:26 ; month:10 ; pages:127-138

Links:	Volltext

DOI / URN:	10.1007/s00180-012-0374-5

Katalog-ID:	SPR001514245

Internformat


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520			\|a Abstract Estimation of probability density functions (PDF) is a fundamental concept in statistics. This paper proposes an ensemble learning approach for density estimation using Gaussian mixture models (GMM). Ensemble learning is closely related to model averaging: While the standard model selection method determines the most suitable single GMM, the ensemble approach uses a subset of GMM which are combined in order to improve precision and stability of the estimated probability density function. The ensemble GMM is theoretically investigated and also numerical experiments were conducted to demonstrate benefits from the model. The results of these evaluations show promising results for classifications and the approximation of non-Gaussian PDF.
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650		4	\|a Density estimation \|7 (dpeaa)DE-He213
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700	1		\|a Schels, Martin \|4 aut
700	1		\|a Schwenker, Friedhelm \|4 aut
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allfields	10.1007/s00180-012-0374-5 doi (DE-627)SPR001514245 (SPR)s00180-012-0374-5-e DE-627 ger DE-627 rakwb eng Glodek, Michael verfasserin aut Ensemble Gaussian mixture models for probability density estimation 2012 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Springer-Verlag Berlin Heidelberg 2012 Abstract Estimation of probability density functions (PDF) is a fundamental concept in statistics. This paper proposes an ensemble learning approach for density estimation using Gaussian mixture models (GMM). Ensemble learning is closely related to model averaging: While the standard model selection method determines the most suitable single GMM, the ensemble approach uses a subset of GMM which are combined in order to improve precision and stability of the estimated probability density function. The ensemble GMM is theoretically investigated and also numerical experiments were conducted to demonstrate benefits from the model. The results of these evaluations show promising results for classifications and the approximation of non-Gaussian PDF. Ensemble learning (dpeaa)DE-He213 Density estimation (dpeaa)DE-He213 Finite mixture models (dpeaa)DE-He213 Schels, Martin aut Schwenker, Friedhelm aut Enthalten in Computational statistics Berlin : Springer, 1999 28(2012), 1 vom: 26. Okt., Seite 127-138 (DE-627)271599170 (DE-600)1480911-4 1613-9658 nnns volume:28 year:2012 number:1 day:26 month:10 pages:127-138 https://dx.doi.org/10.1007/s00180-012-0374-5 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 28 2012 1 26 10 127-138
spelling	10.1007/s00180-012-0374-5 doi (DE-627)SPR001514245 (SPR)s00180-012-0374-5-e DE-627 ger DE-627 rakwb eng Glodek, Michael verfasserin aut Ensemble Gaussian mixture models for probability density estimation 2012 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Springer-Verlag Berlin Heidelberg 2012 Abstract Estimation of probability density functions (PDF) is a fundamental concept in statistics. This paper proposes an ensemble learning approach for density estimation using Gaussian mixture models (GMM). Ensemble learning is closely related to model averaging: While the standard model selection method determines the most suitable single GMM, the ensemble approach uses a subset of GMM which are combined in order to improve precision and stability of the estimated probability density function. The ensemble GMM is theoretically investigated and also numerical experiments were conducted to demonstrate benefits from the model. The results of these evaluations show promising results for classifications and the approximation of non-Gaussian PDF. Ensemble learning (dpeaa)DE-He213 Density estimation (dpeaa)DE-He213 Finite mixture models (dpeaa)DE-He213 Schels, Martin aut Schwenker, Friedhelm aut Enthalten in Computational statistics Berlin : Springer, 1999 28(2012), 1 vom: 26. Okt., Seite 127-138 (DE-627)271599170 (DE-600)1480911-4 1613-9658 nnns volume:28 year:2012 number:1 day:26 month:10 pages:127-138 https://dx.doi.org/10.1007/s00180-012-0374-5 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 28 2012 1 26 10 127-138
allfields_unstemmed	10.1007/s00180-012-0374-5 doi (DE-627)SPR001514245 (SPR)s00180-012-0374-5-e DE-627 ger DE-627 rakwb eng Glodek, Michael verfasserin aut Ensemble Gaussian mixture models for probability density estimation 2012 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Springer-Verlag Berlin Heidelberg 2012 Abstract Estimation of probability density functions (PDF) is a fundamental concept in statistics. This paper proposes an ensemble learning approach for density estimation using Gaussian mixture models (GMM). Ensemble learning is closely related to model averaging: While the standard model selection method determines the most suitable single GMM, the ensemble approach uses a subset of GMM which are combined in order to improve precision and stability of the estimated probability density function. The ensemble GMM is theoretically investigated and also numerical experiments were conducted to demonstrate benefits from the model. The results of these evaluations show promising results for classifications and the approximation of non-Gaussian PDF. Ensemble learning (dpeaa)DE-He213 Density estimation (dpeaa)DE-He213 Finite mixture models (dpeaa)DE-He213 Schels, Martin aut Schwenker, Friedhelm aut Enthalten in Computational statistics Berlin : Springer, 1999 28(2012), 1 vom: 26. Okt., Seite 127-138 (DE-627)271599170 (DE-600)1480911-4 1613-9658 nnns volume:28 year:2012 number:1 day:26 month:10 pages:127-138 https://dx.doi.org/10.1007/s00180-012-0374-5 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 28 2012 1 26 10 127-138
allfieldsGer	10.1007/s00180-012-0374-5 doi (DE-627)SPR001514245 (SPR)s00180-012-0374-5-e DE-627 ger DE-627 rakwb eng Glodek, Michael verfasserin aut Ensemble Gaussian mixture models for probability density estimation 2012 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Springer-Verlag Berlin Heidelberg 2012 Abstract Estimation of probability density functions (PDF) is a fundamental concept in statistics. This paper proposes an ensemble learning approach for density estimation using Gaussian mixture models (GMM). Ensemble learning is closely related to model averaging: While the standard model selection method determines the most suitable single GMM, the ensemble approach uses a subset of GMM which are combined in order to improve precision and stability of the estimated probability density function. The ensemble GMM is theoretically investigated and also numerical experiments were conducted to demonstrate benefits from the model. The results of these evaluations show promising results for classifications and the approximation of non-Gaussian PDF. Ensemble learning (dpeaa)DE-He213 Density estimation (dpeaa)DE-He213 Finite mixture models (dpeaa)DE-He213 Schels, Martin aut Schwenker, Friedhelm aut Enthalten in Computational statistics Berlin : Springer, 1999 28(2012), 1 vom: 26. Okt., Seite 127-138 (DE-627)271599170 (DE-600)1480911-4 1613-9658 nnns volume:28 year:2012 number:1 day:26 month:10 pages:127-138 https://dx.doi.org/10.1007/s00180-012-0374-5 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 28 2012 1 26 10 127-138
allfieldsSound	10.1007/s00180-012-0374-5 doi (DE-627)SPR001514245 (SPR)s00180-012-0374-5-e DE-627 ger DE-627 rakwb eng Glodek, Michael verfasserin aut Ensemble Gaussian mixture models for probability density estimation 2012 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Springer-Verlag Berlin Heidelberg 2012 Abstract Estimation of probability density functions (PDF) is a fundamental concept in statistics. This paper proposes an ensemble learning approach for density estimation using Gaussian mixture models (GMM). Ensemble learning is closely related to model averaging: While the standard model selection method determines the most suitable single GMM, the ensemble approach uses a subset of GMM which are combined in order to improve precision and stability of the estimated probability density function. The ensemble GMM is theoretically investigated and also numerical experiments were conducted to demonstrate benefits from the model. The results of these evaluations show promising results for classifications and the approximation of non-Gaussian PDF. Ensemble learning (dpeaa)DE-He213 Density estimation (dpeaa)DE-He213 Finite mixture models (dpeaa)DE-He213 Schels, Martin aut Schwenker, Friedhelm aut Enthalten in Computational statistics Berlin : Springer, 1999 28(2012), 1 vom: 26. Okt., Seite 127-138 (DE-627)271599170 (DE-600)1480911-4 1613-9658 nnns volume:28 year:2012 number:1 day:26 month:10 pages:127-138 https://dx.doi.org/10.1007/s00180-012-0374-5 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 28 2012 1 26 10 127-138
language	English
source	Enthalten in Computational statistics 28(2012), 1 vom: 26. Okt., Seite 127-138 volume:28 year:2012 number:1 day:26 month:10 pages:127-138
sourceStr	Enthalten in Computational statistics 28(2012), 1 vom: 26. Okt., Seite 127-138 volume:28 year:2012 number:1 day:26 month:10 pages:127-138
format_phy_str_mv	Article
institution	findex.gbv.de
topic_facet	Ensemble learning Density estimation Finite mixture models
isfreeaccess_bool	false
container_title	Computational statistics
authorswithroles_txt_mv	Glodek, Michael @@aut@@ Schels, Martin @@aut@@ Schwenker, Friedhelm @@aut@@
publishDateDaySort_date	2012-10-26T00:00:00Z
hierarchy_top_id	271599170
id	SPR001514245
language_de	englisch
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author	Glodek, Michael
spellingShingle	Glodek, Michael misc Ensemble learning misc Density estimation misc Finite mixture models Ensemble Gaussian mixture models for probability density estimation
authorStr	Glodek, Michael
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topic_title	Ensemble Gaussian mixture models for probability density estimation Ensemble learning (dpeaa)DE-He213 Density estimation (dpeaa)DE-He213 Finite mixture models (dpeaa)DE-He213
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title	Ensemble Gaussian mixture models for probability density estimation
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title_full	Ensemble Gaussian mixture models for probability density estimation
author_sort	Glodek, Michael
journal	Computational statistics
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author_browse	Glodek, Michael Schels, Martin Schwenker, Friedhelm
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author-letter	Glodek, Michael
doi_str_mv	10.1007/s00180-012-0374-5
title_sort	ensemble gaussian mixture models for probability density estimation
title_auth	Ensemble Gaussian mixture models for probability density estimation
abstract	Abstract Estimation of probability density functions (PDF) is a fundamental concept in statistics. This paper proposes an ensemble learning approach for density estimation using Gaussian mixture models (GMM). Ensemble learning is closely related to model averaging: While the standard model selection method determines the most suitable single GMM, the ensemble approach uses a subset of GMM which are combined in order to improve precision and stability of the estimated probability density function. The ensemble GMM is theoretically investigated and also numerical experiments were conducted to demonstrate benefits from the model. The results of these evaluations show promising results for classifications and the approximation of non-Gaussian PDF. © Springer-Verlag Berlin Heidelberg 2012
abstractGer	Abstract Estimation of probability density functions (PDF) is a fundamental concept in statistics. This paper proposes an ensemble learning approach for density estimation using Gaussian mixture models (GMM). Ensemble learning is closely related to model averaging: While the standard model selection method determines the most suitable single GMM, the ensemble approach uses a subset of GMM which are combined in order to improve precision and stability of the estimated probability density function. The ensemble GMM is theoretically investigated and also numerical experiments were conducted to demonstrate benefits from the model. The results of these evaluations show promising results for classifications and the approximation of non-Gaussian PDF. © Springer-Verlag Berlin Heidelberg 2012
abstract_unstemmed	Abstract Estimation of probability density functions (PDF) is a fundamental concept in statistics. This paper proposes an ensemble learning approach for density estimation using Gaussian mixture models (GMM). Ensemble learning is closely related to model averaging: While the standard model selection method determines the most suitable single GMM, the ensemble approach uses a subset of GMM which are combined in order to improve precision and stability of the estimated probability density function. The ensemble GMM is theoretically investigated and also numerical experiments were conducted to demonstrate benefits from the model. The results of these evaluations show promising results for classifications and the approximation of non-Gaussian PDF. © Springer-Verlag Berlin Heidelberg 2012
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title_short	Ensemble Gaussian mixture models for probability density estimation
url	https://dx.doi.org/10.1007/s00180-012-0374-5
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author2	Schels, Martin Schwenker, Friedhelm
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up_date	2024-07-03T23:03:33.978Z
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fullrecord_marcxml	<?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>01000caa a22002652 4500</leader><controlfield tag="001">SPR001514245</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20230327133456.0</controlfield><controlfield tag="007">cr uuu---uuuuu</controlfield><controlfield tag="008">201001s2012 xx \|\|\|\|\|o 00\| \|\|eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.1007/s00180-012-0374-5</subfield><subfield code="2">doi</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)SPR001514245</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(SPR)s00180-012-0374-5-e</subfield></datafield><datafield tag="040" ind1=" " ind2=" "><subfield code="a">DE-627</subfield><subfield code="b">ger</subfield><subfield code="c">DE-627</subfield><subfield code="e">rakwb</subfield></datafield><datafield tag="041" ind1=" " ind2=" "><subfield code="a">eng</subfield></datafield><datafield tag="100" ind1="1" ind2=" "><subfield code="a">Glodek, Michael</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">Ensemble Gaussian mixture models for probability density estimation</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2012</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="a">Text</subfield><subfield code="b">txt</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="a">Computermedien</subfield><subfield code="b">c</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">Online-Ressource</subfield><subfield code="b">cr</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="500" ind1=" " ind2=" "><subfield code="a">© Springer-Verlag Berlin Heidelberg 2012</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">Abstract Estimation of probability density functions (PDF) is a fundamental concept in statistics. 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