Learning with Lq<1 vs L1-Norm Regularisation with Exponentially Many Irrelevant Features. - Ata Kabán; Robert J. Durrant (ECML/PKDD (1), 2008)

When is 'nearest neighbour' meaningful: A converse theorem and implications. - Robert J. Durrant; Ata Kabán (J. Complex., 2009)

A Bound on the Performance of LDA in Randomly Projected Data Spaces. - Robert J. Durrant; Ata Kabán (ICPR, 2010)

Compressed fisher linear discriminant analysis: classification of randomly projected data. - Robert J. Durrant; Ata Kabán (KDD, 2010)

Error bounds for Kernel Fisher Linear Discriminant in Gaussian Hilbert space. - Robert J. Durrant; Ata Kabán (AISTATS, 2012)

A tight bound on the performance of Fisher's linear discriminant in randomly projected data spaces. - Robert John Durrant; Ata Kabán (Pattern Recognit. Lett., 2012)

Random Projections as Regularizers: Learning a Linear Discriminant Ensemble from Fewer Observations than Dimensions. - Robert J. Durrant; Ata Kabán (ACML, 2013)

Dimension-Adaptive Bounds on Compressive FLD Classification. - Ata Kabán; Robert J. Durrant (ALT, 2013)

Towards large scale continuous EDA: a random matrix theory perspective. - Ata Kabán; Jakramate Bootkrajang; Robert John Durrant (GECCO, 2013)

Sharp Generalization Error Bounds for Randomly-projected Classifiers. - Robert J. Durrant; Ata Kabán (ICML (3), 2013)

Learning in high dimensions with projected linear discriminants. - Robert John Durrant (2013)

Random projections as regularizers: learning a linear discriminant from fewer observations than dimensions. - Robert J. Durrant; Ata Kabán (Mach. Learn., 2015)

How effective is Cauchy-EDA in high dimensions? - Momodou L. Sanyang; Robert J. Durrant; Ata Kabán (CEC, 2016)

Toward Large-Scale Continuous EDA: A Random Matrix Theory Perspective. - Ata Kabán; Jakramate Bootkrajang; Robert J. Durrant (Evol. Comput., 2016)

Maximum Margin Principal Components. - Xianghui Luo; Robert J. Durrant (CoRR, 2017)

Foreword: special issue for the journal track of the 8th Asian conference on machine learning (ACML 2016). - Robert J. Durrant; Kee-Eung Kim; Geoffrey Holmes 001; Stephen Marsland; Masashi Sugiyama; Zhi-Hua Zhou (Mach. Learn., 2017)

Maximum Gradient Dimensionality Reduction. - Xianghui Luo; Robert J. Durrant (ICPR, 2018)

Foreword: special issue for the journal track of the 9th Asian Conference on Machine Learning (ACML 2017). - Wee Sun Lee; Robert J. Durrant (Mach. Learn., 2018)

A Diversity-aware Model for Majority Vote Ensemble Accuracy. - Robert John Durrant; Nick Jin Sean Lim (AISTATS, 2020)

Structure from Randomness in Halfspace Learning with the Zero-One Loss. - Ata Kabán; Robert J. Durrant (J. Artif. Intell. Res., 2020)

Learning from evolving data streams through ensembles of random patches. - Heitor Murilo Gomes; Jesse Read; Albert Bifet; Robert J. Durrant (Knowl. Inf. Syst., 2021)