On the stopping criteria for \(k\)-nearest neighbor in positive unlabeled time series classification problems
DOI10.1016/j.ins.2015.07.061zbMath1390.62174OpenAlexW1417750530MaRDI QIDQ1750499
Publication date: 22 May 2018
Published in: Information Sciences (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.1016/j.ins.2015.07.061
transductive learningtime series classification\(k\)-nearest neighborself-trainingpositive unlabeled learning
Time series, auto-correlation, regression, etc. in statistics (GARCH) (62M10) Classification and discrimination; cluster analysis (statistical aspects) (62H30) Stopping times; optimal stopping problems; gambling theory (60G40)
Uses Software
Cites Work
- Unnamed Item
- Software engineering, artificial intelligence, networking and parallel/distributed computing. Selected papers based on the presentations at the 9th ACIS international conference on software engineering, artificial intelligence, networking, and parallel/distributed computing, Phuket, Thialand, August 6--8, 2008.
- Extensions of multiple testing procedures based on Simes' test
- 10.1162/15324430260185583
- Introduction to Semi-Supervised Learning
- Rank Methods for Combination of Independent Experiments in Analysis of Variance
- Dynamic programming algorithm optimization for spoken word recognition
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