The nature of statistical learning theory
The nature of statistical learning theory
Content-Based Image Retrieval at the End of the Early Years
IEEE Transactions on Pattern Analysis and Machine Intelligence
Neural Networks for Pattern Recognition
Neural Networks for Pattern Recognition
Image Processing Handbook, Fourth Edition
Image Processing Handbook, Fourth Edition
A Tutorial on Support Vector Machines for Pattern Recognition
Data Mining and Knowledge Discovery
Training Support Vector Machines: an Application to Face Detection
CVPR '97 Proceedings of the 1997 Conference on Computer Vision and Pattern Recognition (CVPR '97)
Image classification using hybrid neural networks
Proceedings of the 26th annual international ACM SIGIR conference on Research and development in informaion retrieval
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
Multi-class learning from class proportions
Neurocomputing
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This paper presents a two-level stacked generalization scheme composed of three generalizers of support vector machines (SVMs) for image classification. They are color, texture, and high-level concept SVMs. The focus of this paper is to investigate two training strategies based on two-fold cross-validation and non-cross-validation for the proposed classification scheme by evaluating their classification performances, margin of the hyperplane and numbers of support vectors of SVMs. The results show that the non-cross-validation training method performs better, having higher correct classification rates, larger margin of the hyperplane, and smaller numbers of support vectors.