Active set methods for training the Support Vector Machines (SVM) are advantageous since they enable incremental training and, as we show in this research, do not exhibit exponent...
Christopher Sentelle, Georgios C. Anagnostopoulos,...
We propose an active set algorithm to solve the convex quadratic programming (QP) problem which is the core of the support vector machine (SVM) training. The underlying method is ...
Kernel techniques have long been used in SVM to handle linearly inseparable problems by transforming data to a high dimensional space, but training and testing large data sets is ...
Histograms are used in almost every aspect of computer vi-4 4 sion, from visual descriptors to image representations. Histogram Inter-5 5 section Kernel (HIK) and SVM classifiers ...
In this paper, we present an algorithm that can classify large-scale text data with high classification quality and fast training speed. Our method is based on a novel extension o...
Dong Zhuang, Benyu Zhang, Qiang Yang, Jun Yan, Zhe...