 | 2009 |
|---|
| 138 |       | Wray L. Buntine,
Marko Grobelnik,
Dunja Mladenic,
John Shawe-Taylor:
Machine Learning and Knowledge Discovery in Databases, European Conference, ECML PKDD 2009, Bled, Slovenia, September 7-11, 2009, Proceedings, Part I
Springer 2009 |
| 137 | | Wray L. Buntine,
Marko Grobelnik,
Dunja Mladenic,
John Shawe-Taylor:
Machine Learning and Knowledge Discovery in Databases, European Conference, ECML PKDD 2009, Bled, Slovenia, September 7-11, 2009, Proceedings, Part II
Springer 2009 |
| 136 | | Simon Durrant,
David R. Hardoon,
André Brechmann,
John Shawe-Taylor,
Eduardo R. Miranda,
Henning Scheich:
GLM and SVM analyses of neural response to tonal and atonal stimuli: new techniques and a comparison.
Connect. Sci. 21(2&3): 161-175 (2009) |
| 135 | | S. Özögür-Akyüz,
John Shawe-Taylor,
G.-W. Weber,
Z. B. Ögel:
Pattern analysis for the prediction of fungal pro-peptide cleavage sites.
Discrete Applied Mathematics 157(10): 2388-2394 (2009) |
| 2008 |
|---|
| 134 | | Zakria Hussain,
John Shawe-Taylor:
Theory of matching pursuit.
NIPS 2008: 721-728 |
| 133 | | Craig Saunders,
David R. Hardoon,
John Shawe-Taylor,
Gerhard Widmer:
Using string kernels to identify famous performers from their playing style.
Intell. Data Anal. 12(4): 425-440 (2008) |
| 132 | | Marco Gillies,
Xueni Pan,
Mel Slater,
John Shawe-Taylor:
Responsive listening behavior.
Journal of Visualization and Computer Animation 19(5): 579-589 (2008) |
| 2007 |
|---|
| 131 |  | Michael R. Berthold,
John Shawe-Taylor,
Nada Lavrac:
Advances in Intelligent Data Analysis VII, 7th International Symposium on Intelligent Data Analysis, IDA 2007, Ljubljana, Slovenia, September 6-8, 2007, Proceedings
Springer 2007 |
| 130 | | Zhuoran Wang,
John Shawe-Taylor,
Sándor Szedmák:
Kernel Regression Based Machine Translation.
HLT-NAACL (Short Papers) 2007: 185-188 |
| 129 | | Petroula Tsampouka,
John Shawe-Taylor:
Approximate maximum margin algorithms with rules controlled by the number of mistakes.
ICML 2007: 903-910 |
| 128 | | Zakria Hussain,
John Shawe-Taylor:
Using Generalization Error Bounds to Train the Set Covering Machine.
ICONIP (1) 2007: 258-268 |
| 127 | | David R. Hardoon,
Janaina Mourão Miranda,
Michael J. Brammer,
John Shawe-Taylor:
Using Image Stimuli to Drive fMRI Analysis.
ICONIP (1) 2007: 477-486 |
| 126 | | Cédric Archambeau,
Manfred Opper,
Yuan Shen,
Dan Cornford,
John Shawe-Taylor:
Variational Inference for Diffusion Processes.
NIPS 2007 |
| 125 | | Alexander N. Dolia,
Christopher J. Harris,
John Shawe-Taylor,
D. Mike Titterington:
Kernel ellipsoidal trimming.
Computational Statistics & Data Analysis 52(1): 309-324 (2007) |
| 124 | | Yaoyong Li,
John Shawe-Taylor:
Advanced learning algorithms for cross-language patent retrieval and classification.
Inf. Process. Manage. 43(5): 1183-1199 (2007) |
| 123 | | Sándor Szedmák,
John Shawe-Taylor:
Synthesis of maximum margin and multiview learning using unlabeled data.
Neurocomputing 70(7-9): 1254-1264 (2007) |
| 122 | | Amiran Ambroladze,
Emilio Parrado-Hernández,
John Shawe-Taylor:
Complexity of pattern classes and the Lipschitz property.
Theor. Comput. Sci. 382(3): 232-246 (2007) |
| 2006 |
|---|
| 121 | | Craig Saunders,
Marko Grobelnik,
Steve R. Gunn,
John Shawe-Taylor:
Subspace, Latent Structure and Feature Selection, Statistical and Optimization, Perspectives Workshop, SLSFS 2005, Bohinj, Slovenia, February 23-25, 2005, Revised Selected Papers
Springer 2006 |
| 120 | | David R. Hardoon,
Craig Saunders,
Sándor Szedmák,
John Shawe-Taylor:
A Correlation Approach for Automatic Image Annotation.
ADMA 2006: 681-692 |
| 119 | | Petroula Tsampouka,
John Shawe-Taylor:
Constant Rate Approximate Maximum Margin Algorithms.
ECML 2006: 437-448 |
| 118 | | Alexander N. Dolia,
Tijl De Bie,
Christopher J. Harris,
John Shawe-Taylor,
D. M. Titterington:
The Minimum Volume Covering Ellipsoid Estimation in Kernel-Defined Feature Spaces.
ECML 2006: 630-637 |
| 117 | | Sándor Szedmák,
John Shawe-Taylor:
Synthesis of maximum margin and multiview learning using unlabeled data.
ESANN 2006: 479-484 |
| 116 | | Alain Lehmann,
John Shawe-Taylor:
A probabilistic model for text kernels.
ICML 2006: 537-544 |
| 115 | | Amiran Ambroladze,
Emilio Parrado-Hernández,
John Shawe-Taylor:
Tighter PAC-Bayes Bounds.
NIPS 2006: 9-16 |
| 114 | | Yaoyong Li,
John Shawe-Taylor:
Using KCCA for Japanese-English cross-language information retrieval and document classification.
J. Intell. Inf. Syst. 27(2): 117-133 (2006) |
| 113 | | Juho Rousu,
Craig Saunders,
Sándor Szedmák,
John Shawe-Taylor:
Kernel-Based Learning of Hierarchical Multilabel Classification Models.
Journal of Machine Learning Research 7: 1601-1626 (2006) |
| 2005 |
|---|
| 112 | | Matthew Henderson,
John Shawe-Taylor,
Janez Zerovnik:
Mixture of Vector Experts.
ALT 2005: 386-398 |
| 111 | | Petroula Tsampouka,
John Shawe-Taylor:
Analysis of Generic Perceptron-Like Large Margin Classifiers.
ECML 2005: 750-758 |
| 110 | | Juho Rousu,
Craig Saunders,
Sándor Szedmák,
John Shawe-Taylor:
Learning hierarchical multi-category text classification models.
ICML 2005: 744-751 |
| 109 | | Anders Meng,
John Shawe-Taylor:
An Investigation of Feature Models for Music Genre Classification Using the Support Vector Classifier.
ISMIR 2005: 604-609 |
| 108 | | Mark Everingham,
Andrew Zisserman,
Christopher K. I. Williams,
Luc J. Van Gool,
Moray Allan,
Christopher M. Bishop,
Olivier Chapelle,
Navneet Dalal,
Thomas Deselaers,
Gyuri Dorkó,
Stefan Duffner,
Jan Eichhorn,
Jason D. R. Farquhar,
Mario Fritz,
Christophe Garcia,
Tom Griffiths,
Frédéric Jurie,
Daniel Keysers,
Markus Koskela,
Jorma Laaksonen,
Diane Larlus,
Bastian Leibe,
Hongying Meng,
Hermann Ney,
Bernt Schiele,
Cordelia Schmid,
Edgar Seemann,
John Shawe-Taylor,
Amos J. Storkey,
Sándor Szedmák,
Bill Triggs,
Ilkay Ulusoy,
Ville Viitaniemi,
Jianguo Zhang:
The 2005 PASCAL Visual Object Classes Challenge.
MLCW 2005: 117-176 |
| 107 | | Jason D. R. Farquhar,
David R. Hardoon,
Hongying Meng,
John Shawe-Taylor,
Sándor Szedmák:
Two view learning: SVM-2K, Theory and Practice.
NIPS 2005 |
| 106 | | John Shawe-Taylor,
Christopher K. I. Williams,
Nello Cristianini,
Jaz S. Kandola:
On the eigenspectrum of the gram matrix and the generalization error of kernel-PCA.
IEEE Transactions on Information Theory 51(7): 2510-2522 (2005) |
| 105 | | Juho Rousu,
John Shawe-Taylor:
Efficient Computation of Gapped Substring Kernels on Large Alphabets.
Journal of Machine Learning Research 6: 1323-1344 (2005) |
| 104 | | Thore Graepel,
Ralf Herbrich,
John Shawe-Taylor:
PAC-Bayesian Compression Bounds on the Prediction Error of Learning Algorithms for Classification.
Machine Learning 59(1-2): 55-76 (2005) |
| 103 | | Shutao Li,
John Shawe-Taylor:
Comparison and fusion of multiresolution features for texture classification.
Pattern Recognition Letters 26(5): 633-638 (2005) |
| 2004 |
|---|
| 102 | | John Shawe-Taylor,
Yoram Singer:
Learning Theory, 17th Annual Conference on Learning Theory, COLT 2004, Banff, Canada, July 1-4, 2004, Proceedings
Springer 2004 |
| 101 | | Amiran Ambroladze,
John Shawe-Taylor:
Complexity of Pattern Classes and Lipschitz Property.
ALT 2004: 181-193 |
| 100 | | Hongying Meng,
John Shawe-Taylor,
Sándor Szedmák,
Jason D. R. Farquhar:
Support Vector Machine to Synthesise Kernels.
Deterministic and Statistical Methods in Machine Learning 2004: 242-255 |
| 99 | | Craig Saunders,
David R. Hardoon,
John Shawe-Taylor,
Gerhard Widmer:
Using String Kernels to Identify Famous Performers from Their Playing Style.
ECML 2004: 384-395 |
| 98 | | Shutao Li,
John Shawe-Taylor:
Texture Classification by Combining Wavelet and Contourlet Features.
SSPR/SPR 2004: 1126-1134 |
| 97 | | David R. Hardoon,
Sándor Szedmák,
John Shawe-Taylor:
Canonical Correlation Analysis: An Overview with Application to Learning Methods.
Neural Computation 16(12): 2639-2664 (2004) |
| 2003 |
|---|
| 96 | | Jaz S. Kandola,
Thore Graepel,
John Shawe-Taylor:
Reducing Kernel Matrix Diagonal Dominance Using Semi-definite Programming.
COLT 2003: 288-302 |
| 95 | | Amiran Ambroladze,
John Shawe-Taylor:
When Is Small Beautiful?
COLT 2003: 729-730 |
| 94 | | Jure Leskovec,
John Shawe-Taylor:
Linear Programming Boosting for Uneven Datasets.
ICML 2003: 456-463 |
| 93 | | Mario Marchand,
Mohak Shah,
John Shawe-Taylor,
Marina Sokolova:
The Set Covering Machine with Data-Dependent Half-Spaces.
ICML 2003: 520-527 |
| 92 | | Thore Graepel,
Ralf Herbrich,
Andriy Kharechko,
John Shawe-Taylor:
Semi-Definite Programming by Perceptron Learning.
NIPS 2003 |
| 2002 |
|---|
| 91 | | John Shawe-Taylor,
Christopher K. I. Williams,
Nello Cristianini,
Jaz S. Kandola:
On the Eigenspectrum of the Gram Matrix and Its Relationship to the Operator Eigenspectrum.
ALT 2002: 23-40 |
| 90 | | John Shawe-Taylor,
Christopher K. I. Williams,
Nello Cristianini,
Jaz S. Kandola:
On the Eigenspectrum of the Gram Matrix and Its Relationship to the Operator Eigenspectrum.
Discovery Science 2002: 12 |
| 89 | | Yaoyong Li,
Hugo Zaragoza,
Ralf Herbrich,
John Shawe-Taylor,
Jaz S. Kandola:
The Perceptron Algorithm with Uneven Margins.
ICML 2002: 379-386 |
| 88 | | Craig Saunders,
Hauke Tschach,
John Shawe-Taylor:
Syllables and other String Kernel Extensions.
ICML 2002: 530-537 |
| 87 | | Alexei Vinokourov,
John Shawe-Taylor,
Nello Cristianini:
Inferring a Semantic Representation of Text via Cross-Language Correlation Analysis.
NIPS 2002: 1473-1480 |
| 86 | | John Shawe-Taylor,
Christopher K. I. Williams:
The Stability of Kernel Principal Components Analysis and its Relation to the Process Eigenspectrum.
NIPS 2002: 367-374 |
| 85 | | John Langford,
John Shawe-Taylor:
PAC-Bayes & Margins.
NIPS 2002: 423-430 |
| 84 | | Craig Saunders,
John Shawe-Taylor,
Alexei Vinokourov:
String Kernels, Fisher Kernels and Finite State Automata.
NIPS 2002: 633-640 |
| 83 | | Jaz S. Kandola,
John Shawe-Taylor,
Nello Cristianini:
Learning Semantic Similarity.
NIPS 2002: 657-664 |
| 82 | | Marina Sokolova,
Mario Marchand,
Nathalie Japkowicz,
John Shawe-Taylor:
The Decision List Machine.
NIPS 2002: 921-928 |
| 81 | | Nicola Cancedda,
Cyril Goutte,
Jean-Michel Renders,
Nicolò Cesa-Bianchi,
Alex Conconi,
Yaoyong Li,
John Shawe-Taylor,
Alexei Vinokourov,
Thore Graepel,
Claudio Gentile:
Kernel Methods for Document Filtering.
TREC 2002 |
| 80 | | Ying Guo,
Peter L. Bartlett,
John Shawe-Taylor,
Robert C. Williamson:
Covering numbers for support vector machines.
IEEE Transactions on Information Theory 48(1): 239-250 (2002) |
| 79 | | John Shawe-Taylor,
Nello Cristianini:
On the generalization of soft margin algorithms.
IEEE Transactions on Information Theory 48(10): 2721-2735 (2002) |
| 78 | | Huma Lodhi,
Grigoris J. Karakoulas,
John Shawe-Taylor:
Boosting strategy for classification.
Intell. Data Anal. 6(2): 149-174 (2002) |
| 77 | | Nello Cristianini,
John Shawe-Taylor,
Huma Lodhi:
Latent Semantic Kernels.
J. Intell. Inf. Syst. 18(2-3): 127-152 (2002) |
| 76 | | Huma Lodhi,
Craig Saunders,
John Shawe-Taylor,
Nello Cristianini,
Christopher J. C. H. Watkins:
Text Classification using String Kernels.
Journal of Machine Learning Research 2: 419-444 (2002) |
| 75 | | Mario Marchand,
John Shawe-Taylor:
The Set Covering Machine.
Journal of Machine Learning Research 3: 723-746 (2002) |
| 74 | | Ayhan Demiriz,
Kristin P. Bennett,
John Shawe-Taylor:
Linear Programming Boosting via Column Generation.
Machine Learning 46(1-3): 225-254 (2002) |
| 73 | | Yves Van de Peer,
John Shawe-Taylor,
Jayabalan Joseph,
Axel Meyer:
Wanda: a database of duplicated fish genes.
Nucleic Acids Research 30(1): 109-112 (2002) |
| 2001 |
|---|
| 72 | | Thorsten Joachims,
Nello Cristianini,
John Shawe-Taylor:
Composite Kernels for Hypertext Categorisation.
ICML 2001: 250-257 |
| 71 | | Mario Marchand,
John Shawe-Taylor:
Learning with the Set Covering Machine.
ICML 2001: 345-352 |
| 70 | | Nello Cristianini,
John Shawe-Taylor,
Huma Lodhi:
Latent Semantic Kernels.
ICML 2001: 66-73 |
| 69 | | Nello Cristianini,
John Shawe-Taylor,
André Elisseeff,
Jaz S. Kandola:
On Kernel-Target Alignment.
NIPS 2001: 367-373 |
| 68 | | John Shawe-Taylor,
Nello Cristianini,
Jaz S. Kandola:
On the Concentration of Spectral Properties.
NIPS 2001: 511-517 |
| 67 | | Nello Cristianini,
John Shawe-Taylor,
Jaz S. Kandola:
Spectral Kernel Methods for Clustering.
NIPS 2001: 649-655 |
| 66 | | John Shawe-Taylor:
Neural Network Learning: Theoretical Foundation.
AI Magazine 22(2): 99-100 (2001) |
| 65 | | Peter Burge,
John Shawe-Taylor:
An Unsupervised Neural Network Approach to Profiling the Behavior of Mobile Phone Users for Use in Fraud Detection.
J. Parallel Distrib. Comput. 61(7): 915-925 (2001) |
| 64 | | Bernhard Schölkopf,
John C. Platt,
John Shawe-Taylor,
Alex J. Smola,
Robert C. Williamson:
Estimating the Support of a High-Dimensional Distribution.
Neural Computation 13(7): 1443-1471 (2001) |
| 2000 |
|---|
| 63 | | Thore Graepel,
Ralf Herbrich,
John Shawe-Taylor:
Generalisation Error Bounds for Sparse Linear Classifiers.
COLT 2000: 298-303 |
| 62 | | Ralf Herbrich,
Thore Graepel,
John Shawe-Taylor:
Sparsity vs. Large Margins for Linear Classifiers.
COLT 2000: 304-308 |
| 61 | | Kristin P. Bennett,
Ayhan Demiriz,
John Shawe-Taylor:
A Column Generation Algorithm For Boosting.
ICML 2000: 65-72 |
| 60 | | Matthias Rychetsky,
John Shawe-Taylor,
Manfred Glesner:
Direct Bayes Point Machines.
ICML 2000: 815-822 |
| 59 | | Huma Lodhi,
Grigoris J. Karakoulas,
John Shawe-Taylor:
Boosting the Margin Distribution.
IDEAL 2000: 54-59 |
| 58 | | Huma Lodhi,
John Shawe-Taylor,
Nello Cristianini,
Christopher J. C. H. Watkins:
Text Classification using String Kernels.
NIPS 2000: 563-569 |
| 57 | | Barry Rising,
John Shawe-Taylor,
Janez Zerovnik:
Graph Colouring by Maximal Evidence Edge Adding.
PATAT 2000: 294-308 |
| 56 | | Tomaz Pisanski,
John Shawe-Taylor:
Characterizing Graph Drawing with Eigenvectors.
Journal of Chemical Information and Computer Sciences 40(3): 567-571 (2000) |
| 55 | | Kristin P. Bennett,
Nello Cristianini,
John Shawe-Taylor,
Donghui Wu:
Enlarging the Margins in Perceptron Decision Trees.
Machine Learning 41(3): 295-313 (2000) |
| 1999 |
|---|
| 54 | | Ying Guo,
Peter L. Bartlett,
John Shawe-Taylor,
Robert C. Williamson:
Covering Numbers for Support Vector Machines.
COLT 1999: 267-277 |
| 53 | | John Shawe-Taylor,
Nello Cristianini:
Further Results on the Margin Distribution.
COLT 1999: 278-285 |
| 52 | | Nello Cristianini,
Colin Campbell,
John Shawe-Taylor:
A multiplicative updating algorithm for training support vector machine.
ESANN 1999: 189-194 |
| 51 | | John Shawe-Taylor,
Nello Cristianini:
Margin Distribution Bounds on Generalization.
EuroCOLT 1999: 263-273 |
| 50 | | John Shawe-Taylor,
Nello Cristianini:
Generalization Performance of Classifiers in Terms of Observed Covering Numbers.
EuroCOLT 1999: 274-284 |
| 49 | | Donghui Wu,
Kristin P. Bennett,
Nello Cristianini,
John Shawe-Taylor:
Large Margin Trees for Induction and Transduction.
ICML 1999: 474-483 |
| 48 | | Alex J. Smola,
John Shawe-Taylor,
Bernhard Schölkopf,
Robert C. Williamson:
The Entropy Regularization Information Criterion.
NIPS 1999: 342-348 |
| 47 | | John C. Platt,
Nello Cristianini,
John Shawe-Taylor:
Large Margin DAGs for Multiclass Classification.
NIPS 1999: 547-553 |
| 46 | | Bernhard Schölkopf,
Robert C. Williamson,
Alex J. Smola,
John Shawe-Taylor,
John C. Platt:
Support Vector Method for Novelty Detection.
NIPS 1999: 582-588 |
| 45 | | John Shawe-Taylor:
Introducing the Special Issue of Machine Learning Selected from Papers Presented at the 1997 Conference on Computational Learning Theory, COLT'97.
Machine Learning 35(3): 191-192 (1999) |
| 1998 |
|---|
| 44 | | Nello Cristianini,
John Shawe-Taylor,
Peter Sykacek:
Bayesian Classifiers Are Large Margin Hyperplanes in a Hilbert Space.
ICML 1998: 109-117 |
| 43 | | Barry Rising,
Max van Daalen,
John Shawe-Taylor,
Peter Burge,
Janez Zerovnik:
A Neural Accelerator for Graph Colouring Based on an Edge Adding Technique.
NC 1998: 652-656 |
| 42 | | Nello Cristianini,
Colin Campbell,
John Shawe-Taylor:
Dynamically Adapting Kernels in Support Vector Machines.
NIPS 1998: 204-210 |
| 41 | | Grigoris J. Karakoulas,
John Shawe-Taylor:
Optimizing Classifers for Imbalanced Training Sets.
NIPS 1998: 253-259 |
| 40 | | John Shawe-Taylor:
Classification Accuracy Based on Observed Margin.
Algorithmica 22(1/2): 157-172 (1998) |
| 39 | | John Shawe-Taylor:
Special Issue of DAM on the Vapnik-chervonenkis Dimension.
Discrete Applied Mathematics 86(1): 1-2 (1998) |
| 38 | | John Shawe-Taylor,
Peter L. Bartlett,
Robert C. Williamson,
Martin Anthony:
Structural Risk Minimization Over Data-Dependent Hierarchies.
IEEE Transactions on Information Theory 44(5): 1926-1940 (1998) |
| 1997 |
|---|
| 37 | | John Shawe-Taylor,
Robert C. Williamson:
A PAC Analysis of a Bayesian Estimator.
COLT 1997: 2-9 |
| 36 | | John Shawe-Taylor:
Confidence Estimates of Classification Accuracy on New Examples.
EuroCOLT 1997: 260-271 |
| 35 | | Barry Rising,
Max van Daalen,
Peter Burge,
John Shawe-Taylor:
Parallel Graph colouring using FPGAs.
FPL 1997: 121-130 |
| 34 | | John Shawe-Taylor,
Nello Cristianini:
Data-Dependent Structural Risk Minimization for Perceptron Decision Trees.
NIPS 1997 |
| 33 | | Martin Anthony,
John Shawe-Taylor:
A Sufficient Condition for Polynomial Distribution-dependent Learnability.
Discrete Applied Mathematics 77(1): 1-12 (1997) |
| 1996 |
|---|
| 32 | | John Shawe-Taylor,
Peter L. Bartlett,
Robert C. Williamson,
Martin Anthony:
A Framework for Structural Risk Minimisation.
COLT 1996: 68-76 |
| 31 | | Jonathan Baxter,
John Shawe-Taylor:
Learning to Compress Ergodic Sources.
Data Compression Conference 1996: 423 |
| 30 | | Martin Anthony,
Peter L. Bartlett,
Yuval Ishai,
John Shawe-Taylor:
Valid Generalisation from Approximate Interpolation.
Combinatorics, Probability & Computing 5: 191-214 (1996) |
| 29 | | John Shawe-Taylor:
Fast String Matching in Stationary Ergodic Sources.
Combinatorics, Probability & Computing 5: 415-427 (1996) |
| 28 | | Jeffrey Wood,
John Shawe-Taylor:
Representation Theory and Invariant Neural Networks.
Discrete Applied Mathematics 69(1-2): 33-60 (1996) |
| 27 | | Jieyu Zhao,
John Shawe-Taylor,
Max van Daalen:
Learning in Stochastic Bit Stream Neural Networks.
Neural Networks 9(6): 991-998 (1996) |
| 26 | | Jeffrey Wood,
John Shawe-Taylor:
A unifying framework for invariant pattern recognition.
Pattern Recognition Letters 17(14): 1415-1422 (1996) |
| 1995 |
|---|
| 25 | | Carlos Domingo,
John Shawe-Taylor:
The Complexity of Learning Minor Closed Graph Classes.
ALT 1995: 249-260 |
| 24 | | John Shawe-Taylor:
Sample Sizes for Sigmoidal Neural Networks.
COLT 1995: 258-264 |
| 23 | | John Shawe-Taylor,
Jieyu Zhao:
Generalisation of A Class of Continuous Neural Networks.
NIPS 1995: 267-273 |
| 22 | | Martin Anthony,
Graham Brightwell,
John Shawe-Taylor:
On Specifying Boolean Functions by Labelled Examples.
Discrete Applied Mathematics 61(1): 1-25 (1995) |
| 21 | | John Shawe-Taylor:
Sample Sizes for Threshold Networks with Equivalences
Inf. Comput. 118(1): 65-72 (1995) |
| 1994 |
|---|
| 20 | | Patrick W. Fowler,
Tomaz Pisanski,
John Shawe-Taylor:
Molecular Graph Eigenvectors for Molecular Coordinates.
Graph Drawing 1994: 282-285 |
| 19 | | Martin Anthony,
John Shawe-Taylor:
A Result of Vapnik with Applications.
Discrete Applied Mathematics 52(2): 211 (1994) |
| 18 | | John Shawe-Taylor:
Coverings of complete bipartite graphs and associated structures.
Discrete Mathematics 134(1-3): 151-160 (1994) |
| 17 | | Peter Jeavons,
David A. Cohen,
John Shawe-Taylor:
Generating binary sequences for stochastic computing.
IEEE Transactions on Information Theory 40(3): 716-720 (1994) |
| 16 | | John Shawe-Taylor,
Tomaz Pisanski:
Homeomorphism of 2-Complexes is Graph Isomorphism Complete.
SIAM J. Comput. 23(1): 120-132 (1994) |
| 15 | | Jong Yong Kim,
John Shawe-Taylor:
Fast String Matching using an n -gram Algorithm.
Softw., Pract. Exper. 24(1): 79-88 (1994) |
| 1993 |
|---|
| 14 | | Martin Anthony,
John Shawe-Taylor:
Using the Perceptron Algorithm to Find Consistent Hypotheses.
Combinatorics, Probability & Computing 2: 385-387 (1993) |
| 13 | | John Shawe-Taylor,
Martin Anthony,
Norman Biggs:
Bounding Sample Size with the Vapnik-Chervonenkis Dimension.
Discrete Applied Mathematics 42(1): 65-73 (1993) |
| 12 | | Martin Anthony,
John Shawe-Taylor:
A Result of Vapnik with Applications.
Discrete Applied Mathematics 47(3): 207-217 (1993) |
| 1992 |
|---|
| 11 | | Martin Anthony,
Graham Brightwell,
David A. Cohen,
John Shawe-Taylor:
On Exact Specification by Examples.
COLT 1992: 311-318 |
| 10 | | Jong Yong Kim,
John Shawe-Taylor:
Fast Multiple Keyword Searching.
CPM 1992: 41-51 |
| 9 | | John Shawe-Taylor,
Martin Anthony,
Walter Kern:
Classes of feedforward neural networks and their circuit complexity.
Neural Networks 5(6): 971-977 (1992) |
| 8 | | Jong Yong Kim,
John Shawe-Taylor:
An Approximate String-Matching Algorithm.
Theor. Comput. Sci. 92(1): 107-117 (1992) |
| 1991 |
|---|
| 7 | | John Shawe-Taylor:
Threshold Network Learning in the Presence of Equivalences.
NIPS 1991: 879-886 |
| 1990 |
|---|
| 6 | | Martin Anthony,
Norman Biggs,
John Shawe-Taylor:
The Learnability of Formal Concepts.
COLT 1990: 246-257 |
| 5 | | John Shawe-Taylor,
David A. Cohen:
Linear programming algorithm for neural networks.
Neural Networks 3(5): 575-582 (1990) |
| 1987 |
|---|
| 4 | | Chris D. Godsil,
John Shawe-Taylor:
Distance-regularised graphs are distance-regular or distance-biregular.
J. Comb. Theory, Ser. B 43(1): 14-24 (1987) |
| 1985 |
|---|
| 3 | | Bojan Mohar,
John Shawe-Taylor:
Distance-biregular graphs with 2-valent vertices and distance-regular line graphs.
J. Comb. Theory, Ser. B 38(3): 193-203 (1985) |
| 1983 |
|---|
| 2 | | Tomaz Pisanski,
John Shawe-Taylor,
Joze Vrabec:
Edge-colorability of graph bundles.
J. Comb. Theory, Ser. B 35(1): 12-19 (1983) |
| 1981 |
|---|
| 1 | | Tomaz Pisanski,
John Shawe-Taylor:
Search for minimal trivalent cycle permutation graphs with girth nine.
Discrete Mathematics 36(1): 113-115 (1981) |
