4 papers
Bounds for the VC Dimension of 1NN Prototype Sets
Iain A. D. Gunn, Ludmila I. Kuncheva
In Statistical Learning, the Vapnik-Chervonenkis (VC) dimension is an important combinatorial property of classifiers. To our knowledge, no theoretical results yet exist for the VC…
Instance Selection Improves Geometric Mean Accuracy: A Study on Imbalanced Data Classification
Ludmila I. Kuncheva, Álvar Arnaiz-González, José-Francisco Díez-Pastor +1
A natural way of handling imbalanced data is to attempt to equalise the class frequencies and train the classifier of choice on balanced data. For two-class imbalanced problems, th…
Bipartite Graph Matching for Keyframe Summary Evaluation
Iain A. D. Gunn, Ludmila I. Kuncheva, Paria Yousefi
A keyframe summary, or "static storyboard", is a collection of frames from a video designed to summarise its semantic content. Many algorithms have been proposed to extract such su…
On the Evaluation of Video Keyframe Summaries using User Ground Truth
Ludmila I. Kuncheva, Paria Yousefi, Iain A. D. Gunn
Given the great interest in creating keyframe summaries from video, it is surprising how little has been done to formalise their evaluation and comparison. User studies are often c…