Structural extension to logistic regression: Discriminative parameter learning of belief net classifiers

DOI10.1007/s10994-005-0469-0zbMath1101.68759OpenAlexW3136726919MaRDI QIDQ1778137

Bin Shen, Xiaoyuan Su, Russell Greiner, Wei Zhou

Publication date: 17 June 2005

Published in: Machine Learning (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1007/s10994-005-0469-0

zbMATH Keywords

classificationlogistic regressionPAC-learning(Bayesian) belief netscomputational/sample complexity

Mathematics Subject Classification ID

Learning and adaptive systems in artificial intelligence (68T05)

Related Items (13)

Discriminative learning of Bayesian network parameters by differential evolution ⋮ \(\text{ALR}^n\): accelerated higher-order logistic regression ⋮ Bayesian network classifiers ⋮ Sequence classification via large margin hidden Markov models ⋮ Efficient parameter learning of Bayesian network classifiers ⋮ Learning Bayesian network classifiers by risk minimization ⋮ Discriminative vs. Generative Learning of Bayesian Network Classifiers ⋮ Learning mixtures of polynomials of multidimensional probability densities from data using B-spline interpolation ⋮ Discriminative Structure Learning of Markov Logic Networks ⋮ Bayesian classifiers based on kernel density estimation: flexible classifiers ⋮ Stochastic margin-based structure learning of Bayesian network classifiers ⋮ Partition based real-valued encoding scheme for evolutionary algorithms ⋮ Discrete Bayesian Network Classifiers

Uses Software

• UCI-ml
• HandTill2001

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