N
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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Book
An Introduction to Support Vector Machines and Other Kernel-based Learning Methods
TL;DR: This is the first comprehensive introduction to Support Vector Machines (SVMs), a new generation learning system based on recent advances in statistical learning theory, and will guide practitioners to updated literature, new applications, and on-line software.
Book
Kernel Methods for Pattern Analysis
TL;DR: This book provides an easy introduction for students and researchers to the growing field of kernel-based pattern analysis, demonstrating with examples how to handcraft an algorithm or a kernel for a new specific application, and covering all the necessary conceptual and mathematical tools to do so.
Book
An Introduction to Support Vector Machines
TL;DR: This book is the first comprehensive introduction to Support Vector Machines, a new generation learning system based on recent advances in statistical learning theory, and introduces Bayesian analysis of learning and relates SVMs to Gaussian Processes and other kernel based learning methods.
Journal ArticleDOI
Support vector machine classification and validation of cancer tissue samples using microarray expression data
Terrence S. Furey,Nello Cristianini,Nigel Duffy,David W. Bednarski,Michèl Schummer,David Haussler +5 more
TL;DR: A new method to analyse tissue samples using support vector machines for mis-labeled or questionable tissue results and shows that other machine learning methods also perform comparably to the SVM on many of those datasets.
Journal ArticleDOI
Learning the Kernel Matrix with Semidefinite Programming
Gert R. G. Lanckriet,Nello Cristianini,Peter L. Bartlett,Laurent El Ghaoui,Michael I. Jordan +4 more
TL;DR: This paper shows how the kernel matrix can be learned from data via semidefinite programming (SDP) techniques and leads directly to a convex method for learning the 2-norm soft margin parameter in support vector machines, solving an important open problem.