M
Machiko Toyoda
Researcher at Nippon Telegraph and Telephone
Publications - 8
Citations - 106
Machiko Toyoda is an academic researcher from Nippon Telegraph and Telephone. The author has contributed to research in topics: Anomaly detection & Dynamic time warping. The author has an hindex of 5, co-authored 8 publications receiving 83 citations. Previous affiliations of Machiko Toyoda include Nagoya University.
Papers
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Journal ArticleDOI
Pattern discovery in data streams under the time warping distance
TL;DR: The experimental evaluation and case studies show that CrossMatch can incrementally discover common local patterns in data streams within constant time (per update) and space and provide a theoretical analysis and prove that the algorithm does not sacrifice accuracy.
Proceedings ArticleDOI
Adaptive Message Update for Fast Affinity Propagation
TL;DR: The approach, F-AP, is based on two ideas: it computes upper and lower estimates to limit the messages to be updated in each iteration, and it dynamically detects converged messages to efficiently skip unneeded updates.
Journal ArticleDOI
Anomaly detection with inexact labels
TL;DR: A supervised anomaly detection method for data with inexact anomaly labels, where each label, which is assigned to a set of instances, indicates that at least one instance in the set is anomalous, which outperforms existing unsupervised and supervised anomaly Detection and multiple instance learning methods.
Proceedings ArticleDOI
Discovery of cross-similarity in data streams
Machiko Toyoda,Yasushi Sakurai +1 more
TL;DR: This paper proposes a streaming method that efficiently detects partial similarity between sequences using dynamic time warping as a similarity measure and demonstrates that it significantly reduces resources in terms of time and space.
Book ChapterDOI
Identifying Similar Subsequences in Data Streams
TL;DR: This paper proposes a method to detect similar subsequences in streaming fashion that relies on a proposed scoring function that incrementally updates a score, which is suitable for data stream processing and presents an efficient algorithm based on the scoring function.