Posterior probability support vector machines (PPSVMs) prove robust against noises and outliers and need fewer storage support vectors (SVs). Gonen et al. (

2008

2008

) extended PPSVMs to a multiclass case by both single-machine and multimachine approaches. However

these extensions suffer from low classification efficiency

high computational burden

and more importantly

unclassifiable regions. To achieve higher classification efficiency and accuracy with fewer SVs

a binary tree of PPSVMs for the multiclass classification problem is proposed in this letter. Moreover

a Fisher ratio separability measure is adopted to determine the tree structure. Several experiments on handwritten recognition datasets are included to illustrate the proposed approach. Specifically

the Fisher ratio separability accelerated binary tree of PPSVMs obtains overall test accuracy

if not higher than

at least comparable to those of other multiclass algorithms

while using significantly fewer SVs and much less test time.