Presentation | 2003/7/21 Learning the Data Region using Extreme-value Statistics Kazuho WATANABE, Sumio WATANABE, |
---|---|
PDF Download Page | PDF download Page Link |
Abstract(in Japanese) | (See Japanese page) |
Abstract(in English) | In the field of pattern recognition or outlier detection, it is desirable to estimate the region where the data of a particular class are generated. In other words, precise prediction is realized by accurately estimating the support of the distribution that generates the data. Considering the 1-dimensional distribution whose support is a finite interval, the data region is characterized by maximum value and the minimum value in the samples. Limiting distributions of thses values have been studied in the extreme-value theory in statistics. In this research, we propose a method to estimate the data region using the maximum value and the minimum value in the samples. We calculate the average loss of the estimator, and derive the optimal estimators for given loss functions. |
Keyword(in Japanese) | (See Japanese page) |
Keyword(in English) | data region / limiting distribution / extreme value statistics |
Paper # | NC2003-30 |
Date of Issue |
Conference Information | |
Committee | NC |
---|---|
Conference Date | 2003/7/21(1days) |
Place (in Japanese) | (See Japanese page) |
Place (in English) | |
Topics (in Japanese) | (See Japanese page) |
Topics (in English) | |
Chair | |
Vice Chair | |
Secretary | |
Assistant |
Paper Information | |
Registration To | Neurocomputing (NC) |
---|---|
Language | JPN |
Title (in Japanese) | (See Japanese page) |
Sub Title (in Japanese) | (See Japanese page) |
Title (in English) | Learning the Data Region using Extreme-value Statistics |
Sub Title (in English) | |
Keyword(1) | data region |
Keyword(2) | limiting distribution |
Keyword(3) | extreme value statistics |
1st Author's Name | Kazuho WATANABE |
1st Author's Affiliation | Depart of Advanced Applied Electronics, Tokyo Institute of Technology() |
2nd Author's Name | Sumio WATANABE |
2nd Author's Affiliation | P&I Lab, Tokyo Institute of Technology |
Date | 2003/7/21 |
Paper # | NC2003-30 |
Volume (vol) | vol.103 |
Number (no) | 227 |
Page | pp.pp.- |
#Pages | 6 |
Date of Issue |