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ORCIDRobert J. Durrant
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Books and Theses
- 2013
[b1]Robert John Durrant:
Learning in high dimensions with projected linear discriminants. University of Birmingham, UK, 2013
Journal Articles
- 2021
- [j8]Heitor Murilo Gomes
, Jesse Read, Albert Bifet, Robert J. Durrant:
Learning from evolving data streams through ensembles of random patches. Knowl. Inf. Syst. 63(7): 1597-1625 (2021) - 2020
- [j7]Ata Kabán, Robert J. Durrant:
Structure from Randomness in Halfspace Learning with the Zero-One Loss. J. Artif. Intell. Res. 69: 733-764 (2020) - 2018
- [j6]Wee Sun Lee, Robert J. Durrant:
Foreword: special issue for the journal track of the 9th Asian Conference on Machine Learning (ACML 2017). Mach. Learn. 107(4): 639-641 (2018) - 2017
- [j5]Robert J. Durrant, Kee-Eung Kim, Geoffrey Holmes, Stephen Marsland, Masashi Sugiyama, Zhi-Hua Zhou:
Foreword: special issue for the journal track of the 8th Asian conference on machine learning (ACML 2016). Mach. Learn. 106(5): 623-625 (2017) - 2016
- [j4]Ata Kabán, Jakramate Bootkrajang, Robert J. Durrant:
Toward Large-Scale Continuous EDA: A Random Matrix Theory Perspective. Evol. Comput. 24(2): 255-291 (2016) - 2015
- [j3]Robert J. Durrant, Ata Kabán:
Random projections as regularizers: learning a linear discriminant from fewer observations than dimensions. Mach. Learn. 99(2): 257-286 (2015) - 2012
- [j2]Robert John Durrant, Ata Kabán:
A tight bound on the performance of Fisher's linear discriminant in randomly projected data spaces. Pattern Recognit. Lett. 33(7): 911-919 (2012) - 2009
- [j1]Robert J. Durrant, Ata Kabán:
When is 'nearest neighbour' meaningful: A converse theorem and implications. J. Complex. 25(4): 385-397 (2009)
Conference and Workshop Papers
- 2020
- [c11]Robert John Durrant, Nick Jin Sean Lim:
A Diversity-aware Model for Majority Vote Ensemble Accuracy. AISTATS 2020: 4078-4087 - 2018
- [c10]Xianghui Luo, Robert J. Durrant:
Maximum Gradient Dimensionality Reduction. ICPR 2018: 501-506 - 2016
- [c9]Momodou L. Sanyang, Robert J. Durrant, Ata Kabán:
How effective is Cauchy-EDA in high dimensions? CEC 2016: 3409-3416 - 2013
- [c8]Robert J. Durrant, Ata Kabán:
Random Projections as Regularizers: Learning a Linear Discriminant Ensemble from Fewer Observations than Dimensions. ACML 2013: 17-32 - [c7]Ata Kabán, Robert J. Durrant:
Dimension-Adaptive Bounds on Compressive FLD Classification. ALT 2013: 294-308 - [c6]Ata Kabán, Jakramate Bootkrajang, Robert John Durrant:
Towards large scale continuous EDA: a random matrix theory perspective. GECCO 2013: 383-390 - [c5]Robert J. Durrant, Ata Kabán:
Sharp Generalization Error Bounds for Randomly-projected Classifiers. ICML (3) 2013: 693-701 - 2012
- [c4]Robert J. Durrant, Ata Kabán:
Error bounds for Kernel Fisher Linear Discriminant in Gaussian Hilbert space. AISTATS 2012: 337-345 - 2010
- [c3]Robert J. Durrant, Ata Kabán:
A Bound on the Performance of LDA in Randomly Projected Data Spaces. ICPR 2010: 4044-4047 - [c2]Robert J. Durrant, Ata Kabán:
Compressed fisher linear discriminant analysis: classification of randomly projected data. KDD 2010: 1119-1128 - 2008
- [c1]Ata Kabán, Robert J. Durrant:
Learning with Lq<1 vs L1-Norm Regularisation with Exponentially Many Irrelevant Features. ECML/PKDD (1) 2008: 580-596
Editorship
- 2016
- [e1]Robert J. Durrant, Kee-Eung Kim:
Proceedings of The 8th Asian Conference on Machine Learning, ACML 2016, Hamilton, New Zealand, November 16-18, 2016. JMLR Workshop and Conference Proceedings 63, JMLR.org 2016 [contents]
Informal and Other Publications
- 2017
- [i1]Xianghui Luo, Robert J. Durrant:
Maximum Margin Principal Components. CoRR abs/1705.06371 (2017)
Coauthor Index
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