Efficient SVM regression training with SMO

DOI10.1023/A:1012474916001zbMath0998.68107OpenAlexW1482684349MaRDI QIDQ5959963

Steve Lawrence, Gary William Flake

Publication date: 11 April 2002

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

Full work available at URL: https://doi.org/10.1023/a:1012474916001

zbMATH Keywords

sequential minimal optimization algorithm

Mathematics Subject Classification ID

Learning and adaptive systems in artificial intelligence (68T05)

Related Items

A Note on the Decomposition Methods for Support Vector Regression ⋮ A Study on SMO Algorithm for Solving ϵ-SVR with Non-PSD Kernels ⋮ Gradient projection methods for quadratic programs and applications in training support vector machines ⋮ Nonparallel support vector regression model and its SMO-type solver ⋮ Parameter detection of thin films from their X-ray reflectivity by support vector machines. ⋮ ALGORITHM OF ε-SVR BASED ON A LARGE-SCALE SAMPLE SET: STEP-BY-STEP SEARCH ⋮ On the working set selection in gradient projection-based decomposition techniques for support vector machines ⋮ Parameter selection of support vector regression based on hybrid optimization algorithm and its application ⋮ A novel perspective on multiclass classification: regular simplex support vector machine ⋮ Arbitrary Norm Support Vector Machines ⋮ Regression conformal prediction with random forests ⋮ \(\upsilon\)-support vector machine based on discriminant sparse neighborhood preserving embedding ⋮ Training v-Support Vector Regression: Theory and Algorithms

Uses Software

• SVMlight