Binary tree of SVM: a new fast multiclass training and classification algorithm

• Published in: IEEE transactions on neural networks Vol. 17; no. 3; pp. 696 - 704
• Main Authors: Fei, B., Jinbai Liu
• Format: Journal Article
• Language: English
• Published: New York, NY IEEE 01.05.2006; Institute of Electrical and Electronics Engineers
• Subjects: Algorithms; Applied sciences; Artificial Intelligence; Binary tree of support vector machine (BTS); Binary trees; c-BTS; Classification; Classification algorithms; Classification tree analysis; Classifiers; Cluster Analysis; Complexity; Computer science; control theory; systems; Connectionism. Neural networks; Convergence; Error correction codes; Exact sciences and technology; Image Interpretation, Computer-Assisted - methods; Information Storage and Retrieval - methods; multiclass classification; Neural networks; Pattern Recognition, Automated - methods; probabilistic output; support vector machine (SVM); Support vector machine classification; Support vector machines; Testing; Training; Tree graphs; Trees; Upper bound; probabilistic output; Binary tree; multiclass classification; support vector machine (SVM); Classification; Vector support machine; Neural network; Binary tree of support vector machine (BTS); Acyclic graph; Multiclass; Directed graph; c-BTS

We present a new architecture named Binary Tree of support vector machine (SVM), or BTS, in order to achieve high classification efficiency for multiclass problems. BTS and its enhanced version, c-BTS, decrease the number of binary classifiers to the greatest extent without increasing the complexity...