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» Kernels for Periodic Time Series Arising in Astronomy
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PKDD
2009
Springer
103views Data Mining» more  PKDD 2009»
9 years 4 months ago
Kernels for Periodic Time Series Arising in Astronomy
Abstract. We present a method for applying machine learning algorithms to the automatic classiļ¬cation of astronomy star surveys using time series of star brightness. Currently su...
Gabriel Wachman, Roni Khardon, Pavlos Protopapas, ...
DATAMINE
2006
224views more  DATAMINE 2006»
8 years 10 months ago
Characteristic-Based Clustering for Time Series Data
With the growing importance of time series clustering research, particularly for similarity searches amongst long time series such as those arising in medicine or finance, it is cr...
Xiaozhe Wang, Kate A. Smith, Rob J. Hyndman
SAC
2016
ACM
3 years 6 months ago
Nonlinear kernel density principal component analysis with application to climate data
Abstract Principal component analysis (PCA) is a wellestablished tool for identifying the main sources of variation in multivariate data. However, as a linear method it cannot desc...
Seppo Pulkkinen
JMLR
2010
157views more  JMLR 2010»
8 years 5 months ago
Why are DBNs sparse?
Real stochastic processes operating in continuous time can be modeled by sets of stochastic differential equations. On the other hand, several popular model families, including hi...
Shaunak Chatterjee, Stuart Russell
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