Probabilistic neural network with homogeneity testing in recognition of discrete patterns set
The article is devoted to pattern recognition task with the database containing small number of samples per class. By mapping of local continuous feature vectors to a discrete range, this problem is reduced to statistical classification of a set of discrete finite patterns. It is demonstrated that t...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Savchenko, A.V. [verfasserIn] |
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| Format: |
E-Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2013transfer abstract |
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| Schlagwörter: |
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| Umfang: |
15 |
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| Übergeordnetes Werk: |
Enthalten in: Regulatory design for RES-E support mechanisms: Learning curves, market structure, and burden-sharing - 2012, the official journal of the International Neural Network Society, European Neural Network Society and Japanese Neural Network Society, Amsterdam |
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| Übergeordnetes Werk: |
volume:46 ; year:2013 ; pages:227-241 ; extent:15 |
| Links: |
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| DOI / URN: |
10.1016/j.neunet.2013.06.003 |
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ELV033275971 |
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| 520 | |a The article is devoted to pattern recognition task with the database containing small number of samples per class. By mapping of local continuous feature vectors to a discrete range, this problem is reduced to statistical classification of a set of discrete finite patterns. It is demonstrated that the Bayesian decision under the assumption that probability distributions can be estimated using the Parzen kernel and the Gaussian window with a fixed variance for all the classes, implemented in the PNN, is not optimal in the classification of a set of patterns. We presented here the novel modification of the PNN with homogeneity testing which gives an optimal solution of the latter task under the same assumption about probability densities. By exploiting the discrete nature of patterns our modification prevents the well-known drawbacks of the memory-based approach implemented in both the PNN and the PNN with homogeneity testing, namely, low classification speed and high requirements to the memory usage. Our modification only requires the storage and processing of the histograms of input and training samples. We present the results of an experimental study in two practically important tasks: (1) the problem of Russian text authorship attribution with character n -grams features; and (2) face recognition with well-known datasets (AT&T, FERET and JAFFE) and comparison of color- and gradient-orientation histograms. Our results support the statement that the proposed network provides better accuracy (1%–7%) and is much more resistant to change of the smoothing parameter of Gaussian kernel function in comparison with the original PNN. | ||
| 520 | |a The article is devoted to pattern recognition task with the database containing small number of samples per class. By mapping of local continuous feature vectors to a discrete range, this problem is reduced to statistical classification of a set of discrete finite patterns. It is demonstrated that the Bayesian decision under the assumption that probability distributions can be estimated using the Parzen kernel and the Gaussian window with a fixed variance for all the classes, implemented in the PNN, is not optimal in the classification of a set of patterns. We presented here the novel modification of the PNN with homogeneity testing which gives an optimal solution of the latter task under the same assumption about probability densities. By exploiting the discrete nature of patterns our modification prevents the well-known drawbacks of the memory-based approach implemented in both the PNN and the PNN with homogeneity testing, namely, low classification speed and high requirements to the memory usage. Our modification only requires the storage and processing of the histograms of input and training samples. We present the results of an experimental study in two practically important tasks: (1) the problem of Russian text authorship attribution with character n -grams features; and (2) face recognition with well-known datasets (AT&T, FERET and JAFFE) and comparison of color- and gradient-orientation histograms. Our results support the statement that the proposed network provides better accuracy (1%–7%) and is much more resistant to change of the smoothing parameter of Gaussian kernel function in comparison with the original PNN. | ||
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10.1016/j.neunet.2013.06.003 doi GBVA2013014000029.pica (DE-627)ELV033275971 (ELSEVIER)S0893-6080(13)00165-2 DE-627 ger DE-627 rakwb eng 004 004 DE-600 620 VZ 610 VZ 77.50 bkl Savchenko, A.V. verfasserin aut Probabilistic neural network with homogeneity testing in recognition of discrete patterns set 2013transfer abstract 15 nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier The article is devoted to pattern recognition task with the database containing small number of samples per class. By mapping of local continuous feature vectors to a discrete range, this problem is reduced to statistical classification of a set of discrete finite patterns. It is demonstrated that the Bayesian decision under the assumption that probability distributions can be estimated using the Parzen kernel and the Gaussian window with a fixed variance for all the classes, implemented in the PNN, is not optimal in the classification of a set of patterns. We presented here the novel modification of the PNN with homogeneity testing which gives an optimal solution of the latter task under the same assumption about probability densities. By exploiting the discrete nature of patterns our modification prevents the well-known drawbacks of the memory-based approach implemented in both the PNN and the PNN with homogeneity testing, namely, low classification speed and high requirements to the memory usage. Our modification only requires the storage and processing of the histograms of input and training samples. We present the results of an experimental study in two practically important tasks: (1) the problem of Russian text authorship attribution with character n -grams features; and (2) face recognition with well-known datasets (AT&T, FERET and JAFFE) and comparison of color- and gradient-orientation histograms. Our results support the statement that the proposed network provides better accuracy (1%–7%) and is much more resistant to change of the smoothing parameter of Gaussian kernel function in comparison with the original PNN. The article is devoted to pattern recognition task with the database containing small number of samples per class. By mapping of local continuous feature vectors to a discrete range, this problem is reduced to statistical classification of a set of discrete finite patterns. It is demonstrated that the Bayesian decision under the assumption that probability distributions can be estimated using the Parzen kernel and the Gaussian window with a fixed variance for all the classes, implemented in the PNN, is not optimal in the classification of a set of patterns. We presented here the novel modification of the PNN with homogeneity testing which gives an optimal solution of the latter task under the same assumption about probability densities. By exploiting the discrete nature of patterns our modification prevents the well-known drawbacks of the memory-based approach implemented in both the PNN and the PNN with homogeneity testing, namely, low classification speed and high requirements to the memory usage. Our modification only requires the storage and processing of the histograms of input and training samples. We present the results of an experimental study in two practically important tasks: (1) the problem of Russian text authorship attribution with character n -grams features; and (2) face recognition with well-known datasets (AT&T, FERET and JAFFE) and comparison of color- and gradient-orientation histograms. Our results support the statement that the proposed network provides better accuracy (1%–7%) and is much more resistant to change of the smoothing parameter of Gaussian kernel function in comparison with the original PNN. Probabilistic neural network Elsevier Homogeneity testing Elsevier Face recognition Elsevier Authorship attribution Elsevier Discrete patterns set Elsevier Statistical pattern recognition Elsevier Enthalten in Elsevier Regulatory design for RES-E support mechanisms: Learning curves, market structure, and burden-sharing 2012 the official journal of the International Neural Network Society, European Neural Network Society and Japanese Neural Network Society Amsterdam (DE-627)ELV016218965 volume:46 year:2013 pages:227-241 extent:15 https://doi.org/10.1016/j.neunet.2013.06.003 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U SSG-OLC-PHA 77.50 Psychophysiologie VZ AR 46 2013 227-241 15 045F 004 |
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10.1016/j.neunet.2013.06.003 doi GBVA2013014000029.pica (DE-627)ELV033275971 (ELSEVIER)S0893-6080(13)00165-2 DE-627 ger DE-627 rakwb eng 004 004 DE-600 620 VZ 610 VZ 77.50 bkl Savchenko, A.V. verfasserin aut Probabilistic neural network with homogeneity testing in recognition of discrete patterns set 2013transfer abstract 15 nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier The article is devoted to pattern recognition task with the database containing small number of samples per class. By mapping of local continuous feature vectors to a discrete range, this problem is reduced to statistical classification of a set of discrete finite patterns. It is demonstrated that the Bayesian decision under the assumption that probability distributions can be estimated using the Parzen kernel and the Gaussian window with a fixed variance for all the classes, implemented in the PNN, is not optimal in the classification of a set of patterns. We presented here the novel modification of the PNN with homogeneity testing which gives an optimal solution of the latter task under the same assumption about probability densities. By exploiting the discrete nature of patterns our modification prevents the well-known drawbacks of the memory-based approach implemented in both the PNN and the PNN with homogeneity testing, namely, low classification speed and high requirements to the memory usage. Our modification only requires the storage and processing of the histograms of input and training samples. We present the results of an experimental study in two practically important tasks: (1) the problem of Russian text authorship attribution with character n -grams features; and (2) face recognition with well-known datasets (AT&T, FERET and JAFFE) and comparison of color- and gradient-orientation histograms. Our results support the statement that the proposed network provides better accuracy (1%–7%) and is much more resistant to change of the smoothing parameter of Gaussian kernel function in comparison with the original PNN. The article is devoted to pattern recognition task with the database containing small number of samples per class. By mapping of local continuous feature vectors to a discrete range, this problem is reduced to statistical classification of a set of discrete finite patterns. It is demonstrated that the Bayesian decision under the assumption that probability distributions can be estimated using the Parzen kernel and the Gaussian window with a fixed variance for all the classes, implemented in the PNN, is not optimal in the classification of a set of patterns. We presented here the novel modification of the PNN with homogeneity testing which gives an optimal solution of the latter task under the same assumption about probability densities. By exploiting the discrete nature of patterns our modification prevents the well-known drawbacks of the memory-based approach implemented in both the PNN and the PNN with homogeneity testing, namely, low classification speed and high requirements to the memory usage. Our modification only requires the storage and processing of the histograms of input and training samples. We present the results of an experimental study in two practically important tasks: (1) the problem of Russian text authorship attribution with character n -grams features; and (2) face recognition with well-known datasets (AT&T, FERET and JAFFE) and comparison of color- and gradient-orientation histograms. Our results support the statement that the proposed network provides better accuracy (1%–7%) and is much more resistant to change of the smoothing parameter of Gaussian kernel function in comparison with the original PNN. Probabilistic neural network Elsevier Homogeneity testing Elsevier Face recognition Elsevier Authorship attribution Elsevier Discrete patterns set Elsevier Statistical pattern recognition Elsevier Enthalten in Elsevier Regulatory design for RES-E support mechanisms: Learning curves, market structure, and burden-sharing 2012 the official journal of the International Neural Network Society, European Neural Network Society and Japanese Neural Network Society Amsterdam (DE-627)ELV016218965 volume:46 year:2013 pages:227-241 extent:15 https://doi.org/10.1016/j.neunet.2013.06.003 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U SSG-OLC-PHA 77.50 Psychophysiologie VZ AR 46 2013 227-241 15 045F 004 |
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10.1016/j.neunet.2013.06.003 doi GBVA2013014000029.pica (DE-627)ELV033275971 (ELSEVIER)S0893-6080(13)00165-2 DE-627 ger DE-627 rakwb eng 004 004 DE-600 620 VZ 610 VZ 77.50 bkl Savchenko, A.V. verfasserin aut Probabilistic neural network with homogeneity testing in recognition of discrete patterns set 2013transfer abstract 15 nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier The article is devoted to pattern recognition task with the database containing small number of samples per class. By mapping of local continuous feature vectors to a discrete range, this problem is reduced to statistical classification of a set of discrete finite patterns. It is demonstrated that the Bayesian decision under the assumption that probability distributions can be estimated using the Parzen kernel and the Gaussian window with a fixed variance for all the classes, implemented in the PNN, is not optimal in the classification of a set of patterns. We presented here the novel modification of the PNN with homogeneity testing which gives an optimal solution of the latter task under the same assumption about probability densities. By exploiting the discrete nature of patterns our modification prevents the well-known drawbacks of the memory-based approach implemented in both the PNN and the PNN with homogeneity testing, namely, low classification speed and high requirements to the memory usage. Our modification only requires the storage and processing of the histograms of input and training samples. We present the results of an experimental study in two practically important tasks: (1) the problem of Russian text authorship attribution with character n -grams features; and (2) face recognition with well-known datasets (AT&T, FERET and JAFFE) and comparison of color- and gradient-orientation histograms. Our results support the statement that the proposed network provides better accuracy (1%–7%) and is much more resistant to change of the smoothing parameter of Gaussian kernel function in comparison with the original PNN. The article is devoted to pattern recognition task with the database containing small number of samples per class. By mapping of local continuous feature vectors to a discrete range, this problem is reduced to statistical classification of a set of discrete finite patterns. It is demonstrated that the Bayesian decision under the assumption that probability distributions can be estimated using the Parzen kernel and the Gaussian window with a fixed variance for all the classes, implemented in the PNN, is not optimal in the classification of a set of patterns. We presented here the novel modification of the PNN with homogeneity testing which gives an optimal solution of the latter task under the same assumption about probability densities. By exploiting the discrete nature of patterns our modification prevents the well-known drawbacks of the memory-based approach implemented in both the PNN and the PNN with homogeneity testing, namely, low classification speed and high requirements to the memory usage. Our modification only requires the storage and processing of the histograms of input and training samples. We present the results of an experimental study in two practically important tasks: (1) the problem of Russian text authorship attribution with character n -grams features; and (2) face recognition with well-known datasets (AT&T, FERET and JAFFE) and comparison of color- and gradient-orientation histograms. Our results support the statement that the proposed network provides better accuracy (1%–7%) and is much more resistant to change of the smoothing parameter of Gaussian kernel function in comparison with the original PNN. Probabilistic neural network Elsevier Homogeneity testing Elsevier Face recognition Elsevier Authorship attribution Elsevier Discrete patterns set Elsevier Statistical pattern recognition Elsevier Enthalten in Elsevier Regulatory design for RES-E support mechanisms: Learning curves, market structure, and burden-sharing 2012 the official journal of the International Neural Network Society, European Neural Network Society and Japanese Neural Network Society Amsterdam (DE-627)ELV016218965 volume:46 year:2013 pages:227-241 extent:15 https://doi.org/10.1016/j.neunet.2013.06.003 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U SSG-OLC-PHA 77.50 Psychophysiologie VZ AR 46 2013 227-241 15 045F 004 |
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10.1016/j.neunet.2013.06.003 doi GBVA2013014000029.pica (DE-627)ELV033275971 (ELSEVIER)S0893-6080(13)00165-2 DE-627 ger DE-627 rakwb eng 004 004 DE-600 620 VZ 610 VZ 77.50 bkl Savchenko, A.V. verfasserin aut Probabilistic neural network with homogeneity testing in recognition of discrete patterns set 2013transfer abstract 15 nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier The article is devoted to pattern recognition task with the database containing small number of samples per class. By mapping of local continuous feature vectors to a discrete range, this problem is reduced to statistical classification of a set of discrete finite patterns. It is demonstrated that the Bayesian decision under the assumption that probability distributions can be estimated using the Parzen kernel and the Gaussian window with a fixed variance for all the classes, implemented in the PNN, is not optimal in the classification of a set of patterns. We presented here the novel modification of the PNN with homogeneity testing which gives an optimal solution of the latter task under the same assumption about probability densities. By exploiting the discrete nature of patterns our modification prevents the well-known drawbacks of the memory-based approach implemented in both the PNN and the PNN with homogeneity testing, namely, low classification speed and high requirements to the memory usage. Our modification only requires the storage and processing of the histograms of input and training samples. We present the results of an experimental study in two practically important tasks: (1) the problem of Russian text authorship attribution with character n -grams features; and (2) face recognition with well-known datasets (AT&T, FERET and JAFFE) and comparison of color- and gradient-orientation histograms. Our results support the statement that the proposed network provides better accuracy (1%–7%) and is much more resistant to change of the smoothing parameter of Gaussian kernel function in comparison with the original PNN. The article is devoted to pattern recognition task with the database containing small number of samples per class. By mapping of local continuous feature vectors to a discrete range, this problem is reduced to statistical classification of a set of discrete finite patterns. It is demonstrated that the Bayesian decision under the assumption that probability distributions can be estimated using the Parzen kernel and the Gaussian window with a fixed variance for all the classes, implemented in the PNN, is not optimal in the classification of a set of patterns. We presented here the novel modification of the PNN with homogeneity testing which gives an optimal solution of the latter task under the same assumption about probability densities. By exploiting the discrete nature of patterns our modification prevents the well-known drawbacks of the memory-based approach implemented in both the PNN and the PNN with homogeneity testing, namely, low classification speed and high requirements to the memory usage. Our modification only requires the storage and processing of the histograms of input and training samples. We present the results of an experimental study in two practically important tasks: (1) the problem of Russian text authorship attribution with character n -grams features; and (2) face recognition with well-known datasets (AT&T, FERET and JAFFE) and comparison of color- and gradient-orientation histograms. Our results support the statement that the proposed network provides better accuracy (1%–7%) and is much more resistant to change of the smoothing parameter of Gaussian kernel function in comparison with the original PNN. Probabilistic neural network Elsevier Homogeneity testing Elsevier Face recognition Elsevier Authorship attribution Elsevier Discrete patterns set Elsevier Statistical pattern recognition Elsevier Enthalten in Elsevier Regulatory design for RES-E support mechanisms: Learning curves, market structure, and burden-sharing 2012 the official journal of the International Neural Network Society, European Neural Network Society and Japanese Neural Network Society Amsterdam (DE-627)ELV016218965 volume:46 year:2013 pages:227-241 extent:15 https://doi.org/10.1016/j.neunet.2013.06.003 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U SSG-OLC-PHA 77.50 Psychophysiologie VZ AR 46 2013 227-241 15 045F 004 |
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10.1016/j.neunet.2013.06.003 doi GBVA2013014000029.pica (DE-627)ELV033275971 (ELSEVIER)S0893-6080(13)00165-2 DE-627 ger DE-627 rakwb eng 004 004 DE-600 620 VZ 610 VZ 77.50 bkl Savchenko, A.V. verfasserin aut Probabilistic neural network with homogeneity testing in recognition of discrete patterns set 2013transfer abstract 15 nicht spezifiziert zzz rdacontent nicht spezifiziert z rdamedia nicht spezifiziert zu rdacarrier The article is devoted to pattern recognition task with the database containing small number of samples per class. By mapping of local continuous feature vectors to a discrete range, this problem is reduced to statistical classification of a set of discrete finite patterns. It is demonstrated that the Bayesian decision under the assumption that probability distributions can be estimated using the Parzen kernel and the Gaussian window with a fixed variance for all the classes, implemented in the PNN, is not optimal in the classification of a set of patterns. We presented here the novel modification of the PNN with homogeneity testing which gives an optimal solution of the latter task under the same assumption about probability densities. By exploiting the discrete nature of patterns our modification prevents the well-known drawbacks of the memory-based approach implemented in both the PNN and the PNN with homogeneity testing, namely, low classification speed and high requirements to the memory usage. Our modification only requires the storage and processing of the histograms of input and training samples. We present the results of an experimental study in two practically important tasks: (1) the problem of Russian text authorship attribution with character n -grams features; and (2) face recognition with well-known datasets (AT&T, FERET and JAFFE) and comparison of color- and gradient-orientation histograms. Our results support the statement that the proposed network provides better accuracy (1%–7%) and is much more resistant to change of the smoothing parameter of Gaussian kernel function in comparison with the original PNN. The article is devoted to pattern recognition task with the database containing small number of samples per class. By mapping of local continuous feature vectors to a discrete range, this problem is reduced to statistical classification of a set of discrete finite patterns. It is demonstrated that the Bayesian decision under the assumption that probability distributions can be estimated using the Parzen kernel and the Gaussian window with a fixed variance for all the classes, implemented in the PNN, is not optimal in the classification of a set of patterns. We presented here the novel modification of the PNN with homogeneity testing which gives an optimal solution of the latter task under the same assumption about probability densities. By exploiting the discrete nature of patterns our modification prevents the well-known drawbacks of the memory-based approach implemented in both the PNN and the PNN with homogeneity testing, namely, low classification speed and high requirements to the memory usage. Our modification only requires the storage and processing of the histograms of input and training samples. We present the results of an experimental study in two practically important tasks: (1) the problem of Russian text authorship attribution with character n -grams features; and (2) face recognition with well-known datasets (AT&T, FERET and JAFFE) and comparison of color- and gradient-orientation histograms. Our results support the statement that the proposed network provides better accuracy (1%–7%) and is much more resistant to change of the smoothing parameter of Gaussian kernel function in comparison with the original PNN. Probabilistic neural network Elsevier Homogeneity testing Elsevier Face recognition Elsevier Authorship attribution Elsevier Discrete patterns set Elsevier Statistical pattern recognition Elsevier Enthalten in Elsevier Regulatory design for RES-E support mechanisms: Learning curves, market structure, and burden-sharing 2012 the official journal of the International Neural Network Society, European Neural Network Society and Japanese Neural Network Society Amsterdam (DE-627)ELV016218965 volume:46 year:2013 pages:227-241 extent:15 https://doi.org/10.1016/j.neunet.2013.06.003 Volltext GBV_USEFLAG_U GBV_ELV SYSFLAG_U SSG-OLC-PHA 77.50 Psychophysiologie VZ AR 46 2013 227-241 15 045F 004 |
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Enthalten in Regulatory design for RES-E support mechanisms: Learning curves, market structure, and burden-sharing Amsterdam volume:46 year:2013 pages:227-241 extent:15 |
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probabilistic neural network with homogeneity testing in recognition of discrete patterns set |
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Probabilistic neural network with homogeneity testing in recognition of discrete patterns set |
| abstract |
The article is devoted to pattern recognition task with the database containing small number of samples per class. By mapping of local continuous feature vectors to a discrete range, this problem is reduced to statistical classification of a set of discrete finite patterns. It is demonstrated that the Bayesian decision under the assumption that probability distributions can be estimated using the Parzen kernel and the Gaussian window with a fixed variance for all the classes, implemented in the PNN, is not optimal in the classification of a set of patterns. We presented here the novel modification of the PNN with homogeneity testing which gives an optimal solution of the latter task under the same assumption about probability densities. By exploiting the discrete nature of patterns our modification prevents the well-known drawbacks of the memory-based approach implemented in both the PNN and the PNN with homogeneity testing, namely, low classification speed and high requirements to the memory usage. Our modification only requires the storage and processing of the histograms of input and training samples. We present the results of an experimental study in two practically important tasks: (1) the problem of Russian text authorship attribution with character n -grams features; and (2) face recognition with well-known datasets (AT&T, FERET and JAFFE) and comparison of color- and gradient-orientation histograms. Our results support the statement that the proposed network provides better accuracy (1%–7%) and is much more resistant to change of the smoothing parameter of Gaussian kernel function in comparison with the original PNN. |
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The article is devoted to pattern recognition task with the database containing small number of samples per class. By mapping of local continuous feature vectors to a discrete range, this problem is reduced to statistical classification of a set of discrete finite patterns. It is demonstrated that the Bayesian decision under the assumption that probability distributions can be estimated using the Parzen kernel and the Gaussian window with a fixed variance for all the classes, implemented in the PNN, is not optimal in the classification of a set of patterns. We presented here the novel modification of the PNN with homogeneity testing which gives an optimal solution of the latter task under the same assumption about probability densities. By exploiting the discrete nature of patterns our modification prevents the well-known drawbacks of the memory-based approach implemented in both the PNN and the PNN with homogeneity testing, namely, low classification speed and high requirements to the memory usage. Our modification only requires the storage and processing of the histograms of input and training samples. We present the results of an experimental study in two practically important tasks: (1) the problem of Russian text authorship attribution with character n -grams features; and (2) face recognition with well-known datasets (AT&T, FERET and JAFFE) and comparison of color- and gradient-orientation histograms. Our results support the statement that the proposed network provides better accuracy (1%–7%) and is much more resistant to change of the smoothing parameter of Gaussian kernel function in comparison with the original PNN. |
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The article is devoted to pattern recognition task with the database containing small number of samples per class. By mapping of local continuous feature vectors to a discrete range, this problem is reduced to statistical classification of a set of discrete finite patterns. It is demonstrated that the Bayesian decision under the assumption that probability distributions can be estimated using the Parzen kernel and the Gaussian window with a fixed variance for all the classes, implemented in the PNN, is not optimal in the classification of a set of patterns. We presented here the novel modification of the PNN with homogeneity testing which gives an optimal solution of the latter task under the same assumption about probability densities. By exploiting the discrete nature of patterns our modification prevents the well-known drawbacks of the memory-based approach implemented in both the PNN and the PNN with homogeneity testing, namely, low classification speed and high requirements to the memory usage. Our modification only requires the storage and processing of the histograms of input and training samples. We present the results of an experimental study in two practically important tasks: (1) the problem of Russian text authorship attribution with character n -grams features; and (2) face recognition with well-known datasets (AT&T, FERET and JAFFE) and comparison of color- and gradient-orientation histograms. Our results support the statement that the proposed network provides better accuracy (1%–7%) and is much more resistant to change of the smoothing parameter of Gaussian kernel function in comparison with the original PNN. |
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