Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies
if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the
same subfield and year (this is the minimum needed to enter the top 1%, not the average
within it), or reaches the top citation threshold in at least one of its specific research
topics.
An Introduction to Support Vector Machines and Other Kernel-based Learning Methods
20009.8k citationsNello Cristianini, John Shawe‐Taylorprofile →
Kernel Methods for Pattern Analysis
20044.2k citationsJohn Shawe‐Taylor, Nello Cristianiniprofile →
Countries citing papers authored by Nello Cristianini
Since
Specialization
Citations
This map shows the geographic impact of Nello Cristianini's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Nello Cristianini with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Nello Cristianini more than expected).
Fields of papers citing papers by Nello Cristianini
This network shows the impact of papers produced by Nello Cristianini. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Nello Cristianini. The network helps show where Nello Cristianini may publish in the future.
Co-authorship network of co-authors of Nello Cristianini
This figure shows the co-authorship network connecting the top 25 collaborators of Nello Cristianini.
A scholar is included among the top collaborators of Nello Cristianini based on the total number of
citations received by their joint publications. Widths of edges
represent the number of papers authors have co-authored together.
Node borders
signify the number of papers an author published with Nello Cristianini. Nello Cristianini is excluded from
the visualization to improve readability, since they are connected to all nodes in the network.
Júnior, Bruno Ricardo de Castro Leite, et al.. (2013). Improvement of the raw milk microbiological quality by ozone treatment. International Food Research Journal. 20(4). 2017–2021.31 indexed citations
Steinberger, Josef, Marco Turchi, Mijail Kabadjov, Ralf Steinberger, & Nello Cristianini. (2010). Wrapping up a Summary: From Representation to Generation. Meeting of the Association for Computational Linguistics. 382–386.6 indexed citations
11.
Delmestri, Antonella & Nello Cristianini. (2010). Robustness and Statistical Significance of Pam-like Matrices for Cognate Identification. Unitn Eprints Research (Università Degli Studi di Trento). 7(1). 21–31.5 indexed citations
12.
Ricci, Elisa, Tijl De Bie, & Nello Cristianini. (2008). Magic Moments for Structured Output Prediction. Journal of Machine Learning Research. 9(94). 2803–2846.4 indexed citations
13.
Lapa‐Guimarães, Judite, et al.. (2008). Desenvolvimento e aceitação de embutido emulsionado tipo mortadela elaborado com tilápia (Oreochromis niloticus L. ). Hig. aliment. 47–52.3 indexed citations
14.
Kandola, Jaz, Nello Cristianini, & John Shawe‐Taylor. (2002). Learning Semantic Similarity. ePrints Soton (University of Southampton). 15. 673–680.98 indexed citations
15.
Cristianini, Nello, et al.. (2000). Latent Semantic Kernels. ePrints Soton (University of Southampton).31 indexed citations
16.
Lodhi, Huma, John Shawe‐Taylor, Nello Cristianini, & Christopher J. Watkins. (2000). Text Classification using String Kernels. Bristol Research (University of Bristol). 13. 563–569.376 indexed citations
17.
Wu, Donghui, Kristin P. Bennett, Nello Cristianini, & John Shawe‐Taylor. (1999). Large Margin Trees for Induction and Transduction. UCL Discovery (University College London). 474–483.16 indexed citations
18.
Cristianini, Nello, John Shawe‐Taylor, & Peter Sykacek. (1998). Bayesian Classifiers Are Large Margin Hyperplanes in a Hilbert Space. ePrints Soton (University of Southampton). 109–117.7 indexed citations
19.
Cristianini, Nello, Colin Campbell, & John Shawe‐Taylor. (1998). Multiplicative Updatings for Support Vector Learning. ePrints Soton (University of Southampton).3 indexed citations
20.
Cristianini, Nello, et al.. (1998). The Kernel-Adatron Algorithm: A Fast and Simple Learning Procedure for Support Vector Machines. International Conference on Machine Learning. 188–196.164 indexed citations
Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive
bibliographic database. While OpenAlex provides broad and valuable coverage of the global
research landscape, it—like all bibliographic datasets—has inherent limitations. These include
incomplete records, variations in author disambiguation, differences in journal indexing, and
delays in data updates. As a result, some metrics and network relationships displayed in
Rankless may not fully capture the entirety of a scholar's output or impact.