14 citations · 24 across the 9 of their papers we have counts for
9 papers
A Bibliographic View on Constrained Clustering
Ludmila Kuncheva, Francis Williams, Samuel Hennessey
A keyword search on constrained clustering on Web-of-Science returned just under 3,000 documents. We ran automatic analyses of those, and compiled our own bibliography of 183 paper…
Feature Selection from High-Dimensional Data with Very Low Sample Size: A Cautionary Tale
Ludmila I. Kuncheva, Clare E. Matthews, Álvar Arnaiz-González +1
In classification problems, the purpose of feature selection is to identify a small, highly discriminative subset of the original feature set. In many applications, the dataset may…
Basic Ensembles of Vanilla-Style Deep Learning Models Improve Liver Segmentation From CT Images
A. Emre Kavur, Ludmila I. Kuncheva, M. Alper Selver
Segmentation of the liver from 3D computer tomography (CT) images is one of the most frequently performed operations in medical image analysis. In the past decade, Deep Learning Mo…
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…
Learning from Exemplars and Prototypes in Machine Learning and Psychology
Julian Zubek, Ludmila Kuncheva
This paper draws a parallel between similarity-based categorisation models developed in cognitive psychology and the nearest neighbour classifier (1-NN) in machine learning. Concei…
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…