COLT '92 Proceedings of the fifth annual workshop on Computational learning theory
A sequential algorithm for training text classifiers
SIGIR '94 Proceedings of the 17th annual international ACM SIGIR conference on Research and development in information retrieval
The nature of statistical learning theory
The nature of statistical learning theory
Support Vector Machines for 3D Object Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
Pairwise classification and support vector machines
Advances in kernel methods
Combining support vector and mathematical programming methods for classification
Advances in kernel methods
Active learning using adaptive resampling
Proceedings of the sixth ACM SIGKDD international conference on Knowledge discovery and data mining
Active learning: theory and applications
Active learning: theory and applications
Vector projection method for unclassifiable region of support vector machine
Expert Systems with Applications: An International Journal
Reducing number of classifiers in DAGSVM based on class similarity
ICIAP'11 Proceedings of the 16th international conference on Image analysis and processing: Part I
| Hi-index | 0.10 |
Among the SVM-based methods for multi-category classification, "1-a-r", pairwise and DAGSVM are most widely used. The deficiency of "1-a-r" is long training time and unclassifiable region; the deficiency of pairwise and DAGSVM is the redundancy of sub-classifiers. We propose an uncertainty sampling-based multi-category SVM in this paper. In the new method, some necessary sub-classifiers instead of all Nx(N-1)/2 sub-classifiers are selected to be trained and the uncertainty sampling strategy is used to decide which samples should be selected in each training round. This uncertainty sampling-based method is proved to be accurate and efficient by experimental results on the benchmark data.