Incremental Support Vector Learning for Ordinal Regression

• Published in: IEEE transaction on neural networks and learning systems Vol. 26; no. 7; pp. 1403 - 1416
• Main Authors: Bin Gu, Sheng, Victor S., Keng Yeow Tay, Romano, Walter, Shuo Li
• Format: Journal Article
• Language: English
• Published: United States IEEE 01.07.2015; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithm design and analysis; Algorithms; Convergence; Educational institutions; Incremental learning; online learning; Optimization; ordinal regression (OR); support vector machine (SVM); Support vector machines; Training; Training data; Vectors; Incremental learning; ordinal regression (OR); support vector machine (SVM); online learning

Support vector ordinal regression (SVOR) is a popular method to tackle ordinal regression problems. However, until now there were no effective algorithms proposed to address incremental SVOR learning due to the complicated formulations of SVOR. Recently, an interesting accurate on-line algorithm was...