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Maximum Bayes Boundary-Ness Training For Pattern Classification

Published: 21 January 2020 Publication History

Abstract

The ultimate goal of pattern classifier parameter training is to achieve its optimal status (value) that produces Bayes error or a corresponding Bayes boundary. To realize this goal without unrealistically long training repetitions and strict parameter assumptions, the Bayes Boundary-ness-based Selection (BBS) method was recently proposed and its effectiveness was clearly demonstrated. However, the BBS method remains cumbersome because it consists of two stages: the first generates many candidate sets of trained parameters by carefully controlling the training hyperparameters so that those candidate sets can include the optimal target parameter set; the second stage selects an optimal set from candidate sets. To resolve the BBS method's burden, we propose a new one-stage training method that directly optimizes a given classifier parameter set by maximizing its Bayes boundary-ness or increasing its accuracy during Bayes error estimation. We experimentally evaluate our proposed method in terms of its accuracy of Bayes error estimation over four synthetic or real-life datasets. Our experimental results clearly show that it successfully overcomes the drawbacks of the preceding BBS method and directly creates optimal classifier parameter status without generating too many candidate parameter sets.

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  • (2020)Unsupervised Human Activity Recognition Using the Clustering Approach: A ReviewSensors10.3390/s2009270220:9(2702)Online publication date: 9-May-2020

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    SPML '19: Proceedings of the 2019 2nd International Conference on Signal Processing and Machine Learning
    November 2019
    135 pages
    ISBN:9781450372213
    DOI:10.1145/3372806
    Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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    • Ritsumeikan University: Ritsumeikan University

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    Published: 21 January 2020

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    Author Tags

    1. Pattern recognition
    2. class boundary uncertainty
    3. classification

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    • (2020)Unsupervised Human Activity Recognition Using the Clustering Approach: A ReviewSensors10.3390/s2009270220:9(2702)Online publication date: 9-May-2020

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