Real world data mining applications must address the issue of learning from imbalanced data sets. The problem occurs when the number of instances in one class greatly outnumbers t...
In this paper, we investigate an approach based on support vector machines (SVMs) for detection of microcalcification (MC) clusters in digital mammograms, and propose a successive ...
Issam El-Naqa, Yongyi Yang, Miles N. Wernick, Niko...
In human mesenchymal stem cells the envelope surrounding the nucleus, as visualized by the nuclear lamina, has a round and flat shape. The lamina structure is considerably deformed...
Ofer M. Shir, Vered Raz, Roeland W. Dirks, Thomas ...
Linear support vector machines (SVM) are useful for classifying large-scale sparse data. Problems with sparse features are common in applications such as document classification a...
In many pattern recognition applications, high-dimensional feature vectors impose a high computational cost as well as the risk of "overfitting". Feature Selection addre...