ACCELERATING GENERALIZED ITERATIVE SCALING BASED ON STAGGERED AITKEN METHOD FOR ON-LINE CONDITIONAL RANDOM FIELDS

DOI10.1142/S0219691312500592zbMath1261.65064OpenAlexW2080401122MaRDI QIDQ4910879

Seong-Whan Lee, Heung-Il Suk, Hee-Deok Yang

Publication date: 13 March 2013

Published in: International Journal of Wavelets, Multiresolution and Information Processing (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1142/s0219691312500592

zbMATH Keywords

convergencenumerical examplesstochastic optimization methodsAitken accelerationgeneralized iterative scalingsequence labelingon-line conditional random field

Mathematics Subject Classification ID

Random fields (60G60) Numerical mathematical programming methods (65K05) Stochastic programming (90C15)

Cites Work

• Unnamed Item
• Unnamed Item
• On the limited memory BFGS method for large scale optimization
• Simultaneous spotting of signs and fingerspellings based on hierarchical conditional random fields and boostmap embeddings
• Convergence analysis of gradient descent stochastic algorithms
• Periodic step-size adaptation in second-order gradient descent for single-pass on-line structured learning
• Numerical Optimization
• Introduction to Stochastic Search and Optimization
• On‐line learning for very large data sets

This page was built for publication: ACCELERATING GENERALIZED ITERATIVE SCALING BASED ON STAGGERED AITKEN METHOD FOR ON-LINE CONDITIONAL RANDOM FIELDS