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| Presentation | 2013-02-21 15:00 Fast online multivariate non-parametric density estimation under noisy environment Yoshihiro Nakamura, Hiroshi Takahashi, Osamu Hasegawa (Titech)  PRMU2012-141 |
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| Abstract | (in Japanese) | (See Japanese page) |
| (in English) | Recently, with the development of network and sensors technology, a huge number of data are generating continuously in real-time from the real environment. The data from the real environment include in many noise. It is not easy to assume a distribution underlaying the data in advance. Probability density estimation is a critical task in machine learning. But It is difficult to do it for big data in the real world. For handling that huge amount data, we propose fast online multivariate non-parametric density estimator that can deal with noise. Our proposed method is a method that extends Kernel Density Estimation (KDE) and Self-Organizing Incremental Neural Network (SOINN). The proposed method learns data as the network of nodes that are prototypes of samples, and estimates probability density function as the summation of kernel functions located on nodes. Shape and height of a kernel function located on each node is decided adaptively from the local network structure of the node. The result of experiments show that our proposed method outperforms or achieves a comparable performance to the state-of-the-art . |
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| Keyword | (in Japanese) | (See Japanese page) |
| (in English) | kernel density estimation / online learning / Self-Organizing Incremental Neural Network / big data / / / / | |
| Reference Info. | IEICE Tech. Rep., vol. 112, no. 441, PRMU2012-141, pp. 67-72, Feb. 2013. | |
| Paper # | PRMU2012-141 | |
| Date of Issue | 2013-02-14 (PRMU) | |
| ISSN | Print edition: ISSN 0913-5685 Online edition: ISSN 2432-6380 | |
| Copyright and reproduction |
All rights are reserved and no part of this publication may be reproduced or transmitted in any form or by any means, electronic or mechanical, including photocopy, recording, or any information storage and retrieval system, without permission in writing from the publisher. Notwithstanding, instructors are permitted to photocopy isolated articles for noncommercial classroom use without fee. (License No.: 10GA0019/12GB0052/13GB0056/17GB0034/18GB0034) | |
| Download PDF | PRMU2012-141 |
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| Conference Information | |
| Committee | PRMU |
| Conference Date | 2013-02-21 - 2013-02-22 |
| Place (in Japanese) | (See Japanese page) |
| Place (in English) | |
| Topics (in Japanese) | (See Japanese page) |
| Topics (in English) | |
| Paper Information | |
| Registration To | PRMU |
| Conference Code | 2013-02-PRMU |
| Language | Japanese |
| Title (in Japanese) | (See Japanese page) |
| Sub Title (in Japanese) | (See Japanese page) |
| Title (in English) | Fast online multivariate non-parametric density estimation under noisy environment |
| Sub Title (in English) | |
| Keyword(1) | kernel density estimation |
| Keyword(2) | online learning |
| Keyword(3) | Self-Organizing Incremental Neural Network |
| Keyword(4) | big data |
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| 1st Author's Name | Yoshihiro Nakamura |
| 1st Author's Affiliation | Tokyo Institute of Technology (Titech) |
| 2nd Author's Name | Hiroshi Takahashi |
| 2nd Author's Affiliation | Tokyo Institute of Technology (Titech) |
| 3rd Author's Name | Osamu Hasegawa |
| 3rd Author's Affiliation | Tokyo Institute of Technology (Titech) |
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| Speaker | Author-1 |
| Date Time | 2013-02-21 15:00:00 |
| Presentation Time | 30 minutes |
| Registration for | PRMU |
| Paper # | PRMU2012-141 |
| Volume (vol) | vol.112 |
| Number (no) | no.441 |
| Page | pp.67-72 |
| #Pages | 6 |
| Date of Issue | 2013-02-14 (PRMU) |
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