Evolutionary sampling: a novel way of machine learning within a probabilistic framework

DOI10.1016/J.INS.2014.12.001zbMath1360.68722OpenAlexW1977702261WikidataQ115445718 ScholiaQ115445718MaRDI QIDQ528711

Yong-Cai Geng, Sumit K. Garg

Publication date: 16 May 2017

Published in: Information Sciences (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.ins.2014.12.001

zbMATH Keywords

online learningrejection samplingparticle swarm optimizationMonte Carlo Markov chainevolutionary samplingsupport sample model

Mathematics Subject Classification ID

Sampling theory, sample surveys (62D05) Learning and adaptive systems in artificial intelligence (68T05) Problem solving in the context of artificial intelligence (heuristics, search strategies, etc.) (68T20)

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Uses Software

• FRSDE

Cites Work

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• General support vector representation machine for one-class classification of non-stationary classes
• Limit theorems for weighted samples with applications to sequential Monte Carlo methods
• Bandwidth selection: Classical or plug-in?
• Density estimation with stagewise optimization of the empirical risk
• FRSDE: Fast reduced set density estimator using minimal enclosing ball approximation
• Theoretically optimal parameter choices for support vector regression machines with noisy input
• A Brief Survey of Bandwidth Selection for Density Estimation
• Sequential Monte Carlo Samplers
• Hierarchical Dirichlet Processes
• Particle Filtering for Large-Dimensional State Spaces With Multimodal Observation Likelihoods
• Sequential Monte Carlo without likelihoods
• On Estimation of a Probability Density Function and Mode
• Monte Carlo strategies in scientific computing

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