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| Paper Abstract and Keywords | ||
| Presentation | 2017-09-15 10:00 Quantum-Inspired Regression Forest Zeke Xie, Issei Sato (UTokyo)  PRMU2017-40 IBISML2017-12 |
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| Abstract | (in Japanese) | (See Japanese page) |
| (in English) | We propose a Quantum-Inspired Subspace(QIS) Ensemble Method for generating feature ensembles based on feature selections. We assign each principal component a Fraction Transition Probability as its probability weight based on Principal Component Analysis and quantum interpretations. In order to generate the feature subset for each base regressor, we select a feature subset from principal components based on Fraction Transition Probabilities. The idea originating from quantum mechanics can encourage ensemble diversity and the accuracy simultaneously. We incorporate Quantum-Inspired Subspace Method into Random Forest and propose Quantum-Inspired Forest. We theoretically prove that the quantum interpretation corresponds to the first order approximation of ensemble regression. We also evaluate the empirical performance of Quantum-Inspired Forest and Random Forest in multiple hyperparameter settings. Quantum-Inspired Forest prove the significant robustness of the default hyperparameters on most data sets. The contribution of this work is two-fold, a novel ensemble regression algorithm inspired by quantum mechanics and the theoretical connection between quantum interpretations and machine learning algorithms. | |
| Keyword | (in Japanese) | (See Japanese page) |
| (in English) | Supervised Learning / Ensemble Method / Regression Tree / Feature Selection / Quantum Physics / / / | |
| Reference Info. | IEICE Tech. Rep., vol. 117, no. 211, IBISML2017-12, pp. 7-17, Sept. 2017. | |
| Paper # | IBISML2017-12 | |
| Date of Issue | 2017-09-08 (PRMU, IBISML) | |
| 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 | PRMU2017-40 IBISML2017-12 |
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| Conference Information | |
| Committee | PRMU IBISML IPSJ-CVIM |
| Conference Date | 2017-09-15 - 2017-09-16 |
| Place (in Japanese) | (See Japanese page) |
| Place (in English) | |
| Topics (in Japanese) | (See Japanese page) |
| Topics (in English) | |
| Paper Information | |
| Registration To | IBISML |
| Conference Code | 2017-09-PRMU-IBISML-CVIM |
| Language | English |
| Title (in Japanese) | (See Japanese page) |
| Sub Title (in Japanese) | (See Japanese page) |
| Title (in English) | Quantum-Inspired Regression Forest |
| Sub Title (in English) | |
| Keyword(1) | Supervised Learning |
| Keyword(2) | Ensemble Method |
| Keyword(3) | Regression Tree |
| Keyword(4) | Feature Selection |
| Keyword(5) | Quantum Physics |
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| Keyword(7) | |
| Keyword(8) | |
| 1st Author's Name | Zeke Xie |
| 1st Author's Affiliation | The University of Tokyo (UTokyo) |
| 2nd Author's Name | Issei Sato |
| 2nd Author's Affiliation | The University of Tokyo (UTokyo) |
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| Speaker | Author-1 |
| Date Time | 2017-09-15 10:00:00 |
| Presentation Time | 30 minutes |
| Registration for | IBISML |
| Paper # | PRMU2017-40, IBISML2017-12 |
| Volume (vol) | vol.117 |
| Number (no) | no.210(PRMU), no.211(IBISML) |
| Page | pp.7-17 |
| #Pages | 11 |
| Date of Issue | 2017-09-08 (PRMU, IBISML) |
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