Publications

Model Selection

David R Hardoon,

Zakria Hussain,

John Shawe-Taylor,

Learning Non-Linear Feature Maps, With An Application To Representation Learning

Dimitrios Athanasakis,

John Shawe-Taylor,

Delmiro Fernandez-Reyes,

Kernel Methods for Learning

John Shawe-Taylor,

巻頭言「人にやさしいマシン」から「マシンにやさしい人」へ……………………… 山田誠二……… 1 (179)

John Shawe-Taylor,

Nello Cristianini,

Proceedings of the Workshop on On-line Trading of Exploration and Exploitation 2, Bellevue, Washington, USA, July 2, 2011

Dorota Glowacka,

John Shawe-Taylor,

Preface: On-line Trading of Exploration and Exploitation 2

Dorota Glowacka,

John Shawe-Taylor,

Journal of Machine Learning Research-Proceedings Track

Extracting Diagnoses and Investigation Results from Unstructured Text in Electronic

John Shawe-Taylor,

John Shawe-Taylor,

Employing The Complete Face in AVSR to Recover from Facial Occlusions

Benjamin X Hall,

John Shawe-Taylor,

JMLR Workshop and Conference Proceedings

Data-dependent kernels in nearly-linear time

John Shawe-Taylor,

Sparse Canonical Correlation Analysis for Biomarker Discovery: A Case Study in Tuberculosis

Daniel D Agranoff,

John Shawe-Taylor,

Proceedings of the 2011 International Conference on On-line Trading of Exploration and Exploitation 2-Volume 26

Dorota Glowacka,

John Shawe-Taylor,

An Introduction to Statistical Learning Theory

John Shawe-Taylor,

Data-Dependent Geometries and Structures: Analyses and Algorithms for Machine Learning

John Shawe-Taylor,

Advances in Neural Information Processing Systems 24 (NIPS 2011): 25th Annual Conference on Neural Information Processing Systems 2011

John Shawe-Taylor,

Peter Bartlett,

Kilian Weinberger,

Kilian Weinberger,

Morgan Kaufmann Publishers, Inc.

Kernel regression for traffic prediction under missing data

John Shawe-Taylor,

University College London

Proceedings of the 23rd International Conference on Neural Information Processing Systems-Volume 1

John Shawe-Taylor,

Curran Associates Inc.

Proceedings of the First Workshop on Applications of Pattern Analysis September 1-3, 2010, Cumberland Lodge, Windsor, UK

Nello Cristianini,

John Shawe-Taylor,

The magazine archive includes every article published in Communications of the ACM for over the past 50 years.

John Shawe-Taylor,

Description and evaluation of techniques for transfer learning across sub-categories

Teófilo de Campos,

Gabriela Csurka,

Florent Perronnin,

Martin Antenreiter,

Martin Antenreiter,

Guest editors’ introduction: special issue of selected papers from ECML PKDD 2009

Aleksander Kolcz,

Dunja Mladenic,

John Shawe-Taylor,

John Shawe-Taylor,

Springer Nature BV

Learning Theory

John Shawe-Taylor,

Guest editors’ introduction: Special Issue from ECML PKDD 2009

Aleksander Kołcz,

Dunja Mladenić,

John Shawe-Taylor,

John Shawe-Taylor,

Convex Multiview Fisher Discriminant Analysis

John Shawe-Taylor,

Accounting for Voxel Neighbourhood Relationship in the SVM

J Mourao-Miranda,

Analysis of Complex Data Day 2

John Shawe-Taylor,

Description, analysis and evaluation of confidence estimation procedures for sub-categorisation

Teófilo de Campos,

Gabriela Csurka,

Florent Perronnin,

John Shawe-Taylor,

John Shawe-Taylor,

Sentence-level confidence estimation for MT

Nicola Cancedda,

Support Vector Machine Model Selection Using Strangeness

David R Hardoon,

Zakria Hussain,

John Shawe-Taylor,

Analysis of Complex Data

John Shawe-Taylor,

Using generalization error bounds to train the set covering machine

Zakria Hussain,

John Shawe-Taylor,

Springer Berlin Heidelberg

INAUGURAL

John Shawe-Taylor,

A Kernel Canonical Correlation Analysis for Learning the Semantics of Text

John Shawe-Taylor,

Kernel-Based Learning of Hierarchical Multilabel Classification Models (Special Topic on Machine Learning and Optimization)

Managing a Large network of excellence: Case Study of the PASCAL Network

N Cesa-Bianchi,

John Shawe-Taylor,

John Shawe-Taylor,

Learning Hierarchy via Embedding at Two-class Complexity

Sandor Szedmak,

Craig Saunders,

John Shawe-Taylor,

Statistical Aspects of Pattern Analysis

John Shawe-Taylor,

The 2005 PASCAL Visual Object Classes Challenge

Jorma Laaksonen,

Andrew Zisserman,

Andrew Zisserman,

Probabilistic Kernels and Feature Synthesis for Image Categorisation

Jason DR Farquhar,

Sandor Szedmak,

Proceedings of the 2005 international conference on Subspace, Latent Structure and Feature Selection

Craig Saunders,

Marko Grobelnik,

Springer-Verlag

Mixture of vector experts

Matthew Henderson,

John Shawe-Taylor,

Janez Žerovnik,

Springer Berlin Heidelberg

Basic Statistical Learning Theory

John Shawe-Taylor,

The Ingredients of the Fundamental Theorem of Learning

Amiran Ambroladze,

John Shawe-Taylor,

Kernels for structured data: stings, trees, etc

John Shawe-Taylor,

Cambridge University Press

Using KCCA for Japanese-English corss-language information retrieval and classification

John Shawe-Taylor,

Combining Clustering with Canonical Correlation Analysis for Cross-Language Patent Retrieval

John Shawe-Taylor,

µ· ¹º» ¼½¾¿

David R Hardoon,

Sandor Szedmak,

John Shawe-Taylor,

Introduction to the special issue on machine learning methods for text and images

Thomas Hofmann,

When is small beautiful?

Amiran Ambroladze,

John Shawe-Taylor,

Springer Berlin Heidelberg

Kernel Methods for Document Filtering

John Shawe Taylor,

Cancedda Nicola,

Nicolo Cesa Bianchi,

Claudio Gentile,

Claudio Gentile,

National Institute of Standards and Technology

The Stability of Kernel Principal Components Analysis and its Relation to the Eigenspectrum

John Shawe-Taylor,

Chris Williams,

N. Cristianini and J. Shawe-Taylor: An Introduction to Support Vector Machines, Cambridge University Press (2000)

一般社団法人人工知能学会

Graph colouring by maximal evidence edge adding

John Shawe-Taylor,

Janez Žerovnik,

Springer Berlin Heidelberg

Sparsity vs. Large Margins for Linear Classifiers: A Small Sample Size Study

John Shawe-Taylor,

Morgan Kaufmann

Large Margin Classification

John Shawe-Taylor,

Bob Williamson,

Introducing the Special Issue of Machine Learning Selected from Papers Presented at the 1997 Conference on Computational Learning Theory, COLT’97

John Shawe-Taylor,

Kluwer Academic Publishers

+ GMD FIRST

Bernhard Schölkopf,

John Shawe-Taylor,

Institution of Electrical Engineers

A neural accelerator for graph colouring based on an edge adding technique

John Shawe-Taylor,

Report for Publication of the Activity of the Working Group Neural and Computational Learning (NeuroCOLT 8556)

John Shawe-Taylor,

Fraud detection and management in mobile telecommunications

John Shawe-Taylor,

Institution of Engineering and Technology

Royal Holloway University of London, England; Vodafone, England; ESAT. KU Leuven, Belgium; Siemens AG Germany.

John Shawe-Taylor,

Institution of Electrical Engineers