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SDM
2010
SIAM
144views Data Mining» more  SDM 2010»
15 years 6 months ago
A Probabilistic Framework to Learn from Multiple Annotators with Time-Varying Accuracy
This paper addresses the challenging problem of learning from multiple annotators whose labeling accuracy (reliability) differs and varies over time. We propose a framework based ...
Pinar Donmez, Jaime G. Carbonell, Jeff Schneider
TKDE
2008
133views more  TKDE 2008»
15 years 4 months ago
Streaming Time Series Summarization Using User-Defined Amnesic Functions
The past decade has seen a wealth of research on time series representations, because the manipulation, storage, and indexing of large volumes of raw time series data is impractica...
Themis Palpanas, Michail Vlachos, Eamonn J. Keogh,...
ICDM
2010
IEEE
115views Data Mining» more  ICDM 2010»
15 years 2 months ago
Polishing the Right Apple: Anytime Classification Also Benefits Data Streams with Constant Arrival Times
Classification of items taken from data streams requires algorithms that operate in time sensitive and computationally constrained environments. Often, the available time for class...
Jin Shieh, Eamonn J. Keogh
ICDE
2004
IEEE
121views Database» more  ICDE 2004»
16 years 5 months ago
Online Amnesic Approximation of Streaming Time Series
The past decade has seen a wealth of research on time series representations, because the manipulation, storage, and indexing of large volumes of raw time series data is impractic...
Themistoklis Palpanas, Michail Vlachos, Eamonn J. ...
SIGMOD
2007
ACM
186views Database» more  SIGMOD 2007»
16 years 4 months ago
An efficient and accurate method for evaluating time series similarity
A variety of techniques currently exist for measuring the similarity between time series datasets. Of these techniques, the methods whose matching criteria is bounded by a specifi...
Michael D. Morse, Jignesh M. Patel