Algorithms for clustering data
Algorithms for clustering data
Comparison of hierarchic agglomerative clustering methods for document retrieval
The Computer Journal
The nature of statistical learning theory
The nature of statistical learning theory
Nonlinear component analysis as a kernel eigenvalue problem
Neural Computation
Neural Networks: A Comprehensive Foundation
Neural Networks: A Comprehensive Foundation
A robust minimax approach to classification
The Journal of Machine Learning Research
Determining the Number of Clusters/Segments in Hierarchical Clustering/Segmentation Algorithms
ICTAI '04 Proceedings of the 16th IEEE International Conference on Tools with Artificial Intelligence
Cluster Analysis
Multiresolution detection of spiculated lesions in digital mammograms
IEEE Transactions on Image Processing
Input space versus feature space in kernel-based methods
IEEE Transactions on Neural Networks
An introduction to kernel-based learning algorithms
IEEE Transactions on Neural Networks
SEMCCO'11 Proceedings of the Second international conference on Swarm, Evolutionary, and Memetic Computing - Volume Part I
Expert Systems with Applications: An International Journal
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Breast cancer is one of the most common cancers diagnosed in women. Large margin classifiers like the support vector machine (SVM) have been reported effective in computer-assisted diagnosis systems for breast cancers. However, since the separating hyperplane determination exclusively relies on support vectors, the SVM is essentially a local classifier and its performance can be further improved. In this work, we introduce a structured SVM model to determine if each mammographic region is normal or cancerous by considering the cluster structures in the training set. The optimization problem in this new model can be solved efficiently by being formulated as one second order cone programming problem. Experimental evaluation is performed on the Digital Database for Screening Mammography (DDSM) dataset. Various types of features, including curvilinear features, texture features, Gabor features, and multi-resolution features, are extracted from the sample images. We then select the salient features using the recursive feature elimination algorithm. The structured SVM achieves better detection performance compared with a well-tested SVM classifier in terms of the area under the ROC curve.