# 修正
P.18 式: x+z*z/2±z√(x+z*z/4)
# 概要
大きな標本の少頻度の信頼区間を示すことは良いはずだが、信頼区間の計算が複雑なことと、標本のサイズが不明な場合があり、信頼区間が示されることは稀である。本稿では、Wilson score interval による信頼区間の簡易な計算を無限極限にすることでこの問題を解決する。またWilson score intervalをポアソン分布に適用し、その一致を確認する。また経験ベイズによる頻度の予測などを検討する。
# abstruct
It should be good to show low frequency confidence intervals for large samples, but confidence intervals are rarely shown due to the complexity of the confidence interval calculations and the unknown sample size. In this paper, we solve this problem by limiting the simple calculation of confidence intervals by Wilson score interval to the infinite limit. Also, we apply the Wilson score interval to the Poisson distribution and confirm the match. Also, we consider prediction of frequency by empirical Bayes.
統計数理研究所言語系共同研究グループ2020年度第2回合同研究発表会
Presentation slide for AI seminar at Artificial Intelligence Research Center, The National Institute of Advanced Industrial Science and Technology, Japan.
URL (in Japanese): https://www.airc.aist.go.jp/seminar_detail/seminar_046.html
データ拡張 (Data Augmentation) を学習中に使い分けるRefined Data Augmentationについて解説しました。
He, Zhuoxun, et al. "Data augmentation revisited: Rethinking the distribution gap between clean and augmented data." arXiv preprint arXiv:1909.09148 (2019).
This document discusses anomaly detection techniques. It begins with an introduction to anomaly detection and its applications in areas like intrusion detection, fraud detection, and healthcare. It then discusses the use of anomaly detection in AIOps and with graph databases. The document categorizes anomalies as point, contextual, or collective and describes methods for identifying outliers like extreme value analysis. It also discusses techniques for anomaly detection in time series data, including using recurrent neural networks, historical analysis with DBSCAN clustering, and time shift detection using cosine similarity. The document compares pros and cons of time shift detection and DBSCAN for anomaly detection.
Presentation slide for AI seminar at Artificial Intelligence Research Center, The National Institute of Advanced Industrial Science and Technology, Japan.
URL (in Japanese): https://www.airc.aist.go.jp/seminar_detail/seminar_046.html
データ拡張 (Data Augmentation) を学習中に使い分けるRefined Data Augmentationについて解説しました。
He, Zhuoxun, et al. "Data augmentation revisited: Rethinking the distribution gap between clean and augmented data." arXiv preprint arXiv:1909.09148 (2019).
This document discusses anomaly detection techniques. It begins with an introduction to anomaly detection and its applications in areas like intrusion detection, fraud detection, and healthcare. It then discusses the use of anomaly detection in AIOps and with graph databases. The document categorizes anomalies as point, contextual, or collective and describes methods for identifying outliers like extreme value analysis. It also discusses techniques for anomaly detection in time series data, including using recurrent neural networks, historical analysis with DBSCAN clustering, and time shift detection using cosine similarity. The document compares pros and cons of time shift detection and DBSCAN for anomaly detection.
Tandem connectionist anomaly detection: Use of faulty vibration signals in fe...pcl-lab
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This document proposes a method called tandem connectionist anomaly detection that uses faulty vibration data from non-target machines to improve anomaly detection performance on a target machine. The method uses a deep neural network trained on both normal and faulty non-target data to learn discriminative features, which are then used as input to a Gaussian mixture model anomaly detector trained on normal target data. Experiments show this method significantly improves anomaly detection compared to using hand-crafted features or transferring just the detector. It demonstrates the ability to transfer the system between machines of both the same type and different types.
Adaptive training of vibration-based anomaly detector for wind turbine condit...pcl-lab
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Takanori Hasegawa, Jun Ogata, Masahiro Murakawa, Tetsunori Kobayashi, Tetsuji Ogawa, ``Adaptive training of vibration-based anomaly detector for wind turbine condition monitoring,’’ Proc. Annual Conference on PHM Society, pp.177-184, Oct. 2017.