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Nello Cristianini
Researcher at University of Bristol
Publications - 187
Citations - 48616
Nello Cristianini is an academic researcher from University of Bristol. The author has contributed to research in topics: Kernel method & Support vector machine. The author has an hindex of 51, co-authored 183 publications receiving 46640 citations. Previous affiliations of Nello Cristianini include Royal Holloway, University of London & University of California, Davis.
Papers
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Proceedings Article
Bayesian Classifiers Are Large Margin Hyperplanes in a Hilbert Space
TL;DR: A novel theoretical analysis of classiiers of Bayesian algorithms for Neural Networks, based on Data-Dependent VC theory, proves that they can be expected to be large margin hyper-planes in a Hilbert space, and presents experimental evidence that the predictions of the model are correct.
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Learning to translate: a statistical and computational analysis
TL;DR: An extensive experimental study of Phrase-based Statistical Machine Translation, from the point of view of its learning capabilities, which confirms existing and mostly unpublished beliefs about the learning capabilities and provides insight into the way statistical machine translation learns from data.
Posted Content
Machine Decisions and Human Consequences
TL;DR: The discussion here focuses primarily on the case of enforcement decisions in the criminal justice system, but draws on similar situations emerging from other algorithms utilised in controlling access to opportunities, to explain how machine learning works and, as a result, how decisions are made by modern intelligent algorithms or 'classifiers'.
Automating quantitative narrative analysis of news data
TL;DR: A working system for large scale quantitative narrative analysis of news corpora, which includes various recent ideas from text mining and pattern analysis in order to solve a problem arising in computational social sciences is presented.
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2010 Special Issue: Are we there yet?
TL;DR: The idea of generating non-trivial behaviour by analysing vast amounts of data has enabled recommendation systems, search engines, spam filters, optical character recognition, machine translation and speech recognition, among other things.