A distributed ensemble of relevance vector machines for large-scale data sets on Spark

DOI10.1007/S00500-021-05671-YzbMath1498.68258OpenAlexW3135238933MaRDI QIDQ2098338

Yanyan Li

Publication date: 18 November 2022

Published in: Soft Computing (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1007/s00500-021-05671-y

zbMATH Keywords

diversityAdaBoostensemble learningrelevance vector machinelarge-scale data setsSpark

Mathematics Subject Classification ID

Classification and discrimination; cluster analysis (statistical aspects) (62H30) Learning and adaptive systems in artificial intelligence (68T05)

Related Items (5)

A new approach to finite-time adaptive stabilization of high-order uncertain nonlinear system ⋮ A robust low-complexity tracker design with preassigned performance for uncertain high-order nonlinear systems with unknown time-varying delays and high powers ⋮ State-feedback stabilization for stochastic high-order nonlinear systems with time-varying delays ⋮ Global stabilization of high-order time-delay nonlinear systems under a weaker condition ⋮ Global state feedback stabilization of high-order nonlinear systems with multiple time-varying delays

Uses Software

• Hadoop
• GraphLab
• MLlib
• Apache Spark

Cites Work

• Unnamed Item
• An analysis of diversity measures
• Measures of diversity in classifier ensembles and their relationship with the ensemble accuracy
• Accelerating relevance-vector-machine-based classification of hyperspectral image with parallel computing
• Sparse On-Line Gaussian Processes
• 10.1162/15324430152748236

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