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Found 5 papers, 0 papers with code
Feature Selection from High-Dimensional Data with Very Low Sample Size: A Cautionary Tale
In classification problems, the purpose of feature selection is to identify a small, highly discriminative subset of the original feature set.
Bounds for the VC Dimension of 1NN Prototype Sets
We collect some relevant results and use them to provide explicit lower and upper bounds for the VC dimension of 1NN classifiers with a prototype set of fixed size.
Instance Selection Improves Geometric Mean Accuracy: A Study on Imbalanced Data Classification
A natural way of handling imbalanced data is to attempt to equalise the class frequencies and train the classifier of choice on balanced data.
On the Evaluation of Video Keyframe Summaries using User Ground Truth
Here we propose a discrimination capacity measure as a formal way to quantify the improvement over the uniform baseline, assuming that one or more ground truth summaries are available.
Bipartite Graph Matching for Keyframe Summary Evaluation
A keyframe summary, or "static storyboard", is a collection of frames from a video designed to summarise its semantic content.
