A time-series modeling method based on the boosting gradient-descent theory
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Publication:412870
DOI10.1007/S11431-011-4340-1zbMath1236.62120OpenAlexW2063774093MaRDI QIDQ412870
Publication date: 4 May 2012
Published in: Science China. Technological Sciences (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.1007/s11431-011-4340-1
Inference from stochastic processes and prediction (62M20) Time series, auto-correlation, regression, etc. in statistics (GARCH) (62M10) Learning and adaptive systems in artificial intelligence (68T05)
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Cites Work
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- Greedy function approximation: A gradient boosting machine.
- Modal identification of system driven by Lévy random excitation based on continuous time AR model
- Optimization based scheduling for a class of production systems with integral constraints
- Time-varying parameter auto-regressive models for autocovariance nonstationary time series
- Modal parameter identification under non-stationary ambient excitation based on continuous time AR model
- A decision-theoretic generalization of on-line learning and an application to boosting
- Boosting the margin: a new explanation for the effectiveness of voting methods
- Boosting a weak learning algorithm by majority
- Improved boosting algorithms using confidence-rated predictions
- Almansi decomposition for Dunkl operators
- A theory of the learnable
- AN INTRODUCTION TO LONG-MEMORY TIME SERIES MODELS AND FRACTIONAL DIFFERENCING
- Cryptographic limitations on learning Boolean formulae and finite automata
- Boosting and Other Ensemble Methods
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