Testing the performance of some nonparametric pattern recognition algorithms in realistic cases
The success obtained by Statistical Pattern Recognition in many disciplines is certainly related to the quality and availability of many data, normally distributed. However, in other disciplines, the data sets consist of few measurements, often binned, correlated, and not normally distributed. Usual...
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| Vydáno v: | Pattern recognition Ročník 37; číslo 3; s. 447 - 461 |
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| Hlavní autoři: | , |
| Médium: | Journal Article |
| Jazyk: | angličtina |
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Elsevier Ltd
01.03.2004
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| ISSN: | 0031-3203, 1873-5142 |
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| Abstract | The success obtained by Statistical Pattern Recognition in many disciplines is certainly related to the quality and availability of many data, normally distributed. However, in other disciplines, the data sets consist of few measurements, often binned, correlated, and not normally distributed. Usually, we do not even know which features have an influence on the process. The main goal of this paper is to evaluate the performance of some nonparametric Pattern Recognition algorithms when applied to such data. Finally we show the results of the application of the four nonparametric statistical pattern recognition techniques to real volcanological data. |
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| AbstractList | The success obtained by Statistical Pattern Recognition in many disciplines is certainly related to the quality and availability of many data, normally distributed. However, in other disciplines, the data sets consist of few measurements, often binned, correlated, and not normally distributed. Usually, we do not even know which features have an influence on the process. The main goal of this paper is to evaluate the performance of some nonparametric Pattern Recognition algorithms when applied to such data. Finally we show the results of the application of the four nonparametric statistical pattern recognition techniques to real volcanological data. |
| Author | Sandri, Laura Marzocchi, Warner |
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| Cites_doi | 10.1016/S0031-3203(97)00110-6 10.1016/0377-0273(92)90087-T 10.1016/S0031-3203(97)00108-8 10.1109/TPAMI.1980.4767011 10.1016/B978-0-08-047865-4.50007-7 10.1016/S0031-3203(99)00068-0 10.1016/0031-3203(78)90008-0 10.1016/0031-3203(79)90036-0 10.1016/S0031-3203(01)00107-8 10.1016/0031-3203(80)90029-1 10.1785/BSSA0770062125 10.1109/34.75512 10.1016/S0031-3203(98)00097-1 10.1016/S0031-3203(99)00053-9 10.1007/978-1-4612-1096-2_1 |
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| Keywords | Nonparametric Pattern Recognition Optimal subset of features Volcanological data Synthetic data |
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| References | R.O. Duda, P.E. Hart, Pattern Classification and Scene Analysis, Wiley, New York, 1973, p. 482. J.G. Kalbfleisch, Probability and Statistical Inference, Vol. 1, 2nd Edition, Springer, Berlin, 1985, p. 343. Rounds (BIB12) 1980; 12 J.D. Gibbons, Nonparametric Statistical Inference, McGraw-Hill Book Company, New York, 1971, p. 306. Raudys (BIB10) 1979; 11 Jain, Waller (BIB8) 1978; 10 Mulargia, Marzocchi, Gasperini (BIB13) 1992; 53 Raudys, Jain (BIB7) 1991; 13 Hattori, Takahashi (BIB3) 2000; 33 Hattori, Takahashi (BIB4) 1999; 32 Sierra (BIB14) 2002; 35 Skurichina, Duin (BIB11) 1998; 31 Tinti, Mulargia (BIB15) 1987; 77 K. Fukunaga, Introduction to Statistical Pattern Recognition, 2nd Edition, Academic Press Inc., New York, 1990, p. 591. Raudys, Pikelis (BIB9) 1980; PAMI-2 Poston, Marchette (BIB5) 1998; 31 Lotlikar, Kothari (BIB6) 2000; 33 Skurichina (10.1016/j.patcog.2003.08.009_BIB11) 1998; 31 Hattori (10.1016/j.patcog.2003.08.009_BIB4) 1999; 32 Raudys (10.1016/j.patcog.2003.08.009_BIB9) 1980; PAMI-2 Poston (10.1016/j.patcog.2003.08.009_BIB5) 1998; 31 Raudys (10.1016/j.patcog.2003.08.009_BIB7) 1991; 13 Jain (10.1016/j.patcog.2003.08.009_BIB8) 1978; 10 Mulargia (10.1016/j.patcog.2003.08.009_BIB13) 1992; 53 Sierra (10.1016/j.patcog.2003.08.009_BIB14) 2002; 35 10.1016/j.patcog.2003.08.009_BIB16 10.1016/j.patcog.2003.08.009_BIB17 Lotlikar (10.1016/j.patcog.2003.08.009_BIB6) 2000; 33 10.1016/j.patcog.2003.08.009_BIB2 Hattori (10.1016/j.patcog.2003.08.009_BIB3) 2000; 33 Rounds (10.1016/j.patcog.2003.08.009_BIB12) 1980; 12 Raudys (10.1016/j.patcog.2003.08.009_BIB10) 1979; 11 Tinti (10.1016/j.patcog.2003.08.009_BIB15) 1987; 77 10.1016/j.patcog.2003.08.009_BIB1 |
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Fukunaga, Introduction to Statistical Pattern Recognition, 2nd Edition, Academic Press Inc., New York, 1990, p. 591. – volume: 32 start-page: 425 year: 1999 end-page: 432 ident: BIB4 article-title: A new nearest-neighbor rule in the pattern classification problem publication-title: Pattern Recognition – volume: 33 start-page: 521 year: 2000 end-page: 528 ident: BIB3 article-title: A new edited k-nearest neighbor rule in the pattern classification problem publication-title: Pattern Recognition – volume: PAMI-2 start-page: 242 year: 1980 end-page: 252 ident: BIB9 article-title: On dimensionality, sample size, classification error, and complexity of classification algorithm in pattern recognition publication-title: IEEE Trans. Pattern Anal. Mach. Intell. – reference: J.D. Gibbons, Nonparametric Statistical Inference, McGraw-Hill Book Company, New York, 1971, p. 306. – reference: J.G. 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| SubjectTerms | Nonparametric Pattern Recognition Optimal subset of features Synthetic data Volcanological data |
| Title | Testing the performance of some nonparametric pattern recognition algorithms in realistic cases |
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