Mahesan Niranjan

2008
20	EE	Yang Zhang, HongYu Li, Mahesan Niranjan, Peter Rockett: Applying Cost-Sensitive Multiobjective Genetic Programming to Feature Extraction for Spam E-mail Filtering. EuroGP 2008: 325-336
19	EE	Renata da Silva Camargo, Mahesan Niranjan: Mining Protein Database using Machine Learning Techniques. J. Integrative Bioinformatics 5(2): (2008)
18	EE	Sujimarn Suwannaroj, Mahesan Niranjan: Enhancing Automatic Construction of Gene Subnetworks by Integrating Multiple Sources of Information. Signal Processing Systems 50(3): 331-340 (2008)
2005
17		Joab Winkler, Mahesan Niranjan, Neil D. Lawrence: Deterministic and Statistical Methods in Machine Learning, First International Workshop, Sheffield, UK, September 7-10, 2004, Revised Lectures Springer 2005
2004
16	EE	Neil D. Lawrence, Marta Milo, Mahesan Niranjan, Penny Rashbass, Stephan Soullier: Reducing the variability in cDNA microarray image processing by Bayesian inference. Bioinformatics 20(4): (2004)
2003
15	EE	Si Wu, Danmei Chen, Mahesan Niranjan, Shun-ichi Amari: Sequential Bayesian Decoding with a Population of Neurons. Neural Computation 15(5): 993-1012 (2003)
2001
14	EE	Gaafar M. K. Saleh, Mahesan Niranjan: Speech enhancement using a Bayesian evidence approach. Computer Speech & Language 15(2): 101-125 (2001)
2000
13		João F. G. de Freitas, Mahesan Niranjan, Andrew H. Gee: Hierarchical Bayesian Models for Regularization in Sequential Learning. Neural Computation 12(4): 933-953 (2000)
12		João F. G. de Freitas, Mahesan Niranjan, Andrew H. Gee, Arnaud Doucet: Sequential Monte Carlo Methods to Train Neural Network Models. Neural Computation 12(4): 955-993 (2000)
11	EE	João F. G. de Freitas, Mahesan Niranjan, Andrew H. Gee: Dynamic Learning with the EM Algorithm for Neural Networks. VLSI Signal Processing 26(1-2): 119-131 (2000)
1999
10	EE	Gavin Smith, João F. G. de Freitas, Tony Robinson, Mahesan Niranjan: Speech Modelling Using Subspace and EM Techniques. NIPS 1999: 796-802
1998
9	EE	Martin J. J. Scott, Mahesan Niranjan, Richard W. Prager: Realisable Classifiers: Improving Operating Performance on Variable Cost Problems. BMVC 1998
8	EE	João F. G. de Freitas, Mahesan Niranjan, Arnaud Doucet, Andrew H. Gee: Global Optimisation of Neural Network Models via Sequential Sampling. NIPS 1998: 410-416
7	EE	D. R. Lovell, Christopher R. Dance, Mahesan Niranjan, Richard W. Prager, Kevin J. Dalton, R. Derom: Feature selection using expected attainable discrimination. Pattern Recognition Letters 19(5-6): 393-402 (1998)
1997
6		João F. G. de Freitas, Mahesan Niranjan, Andrew H. Gee: Regularisation in Sequential Learning Algorithms. NIPS 1997
5	EE	Sean B. Holden, Mahesan Niranjan: Average-Case Learning Curves for Radial Basis Function Networks. Neural Computation 9(2): 441-460 (1997)
1996
4	EE	Mahesan Niranjan: Sequential Tracking in Pricing Financial Options using Model Based and Neural Network Approaches. NIPS 1996: 960-966
1995
3	EE	Sean B. Holden, Mahesan Niranjan: On the practical applicability of VC dimension bounds. Neural Computation 7(6): 1265-1288 (1995)
1990
2		Mahesan Niranjan, Frank Fallside: Speech Feature Extraction Using Neural Networks. EURASIP Workshop 1990: 197-204
1	EE	Visakan Kadirkamanathan, Mahesan Niranjan, Frank Fallside: Sequential Adaptation of Radial Basis Function Networks. NIPS 1990: 721-727

Coauthor Index

1	Shun-ichi Amari	[15]
2	Renata da Silva Camargo	[19]
3	Danmei Chen	[15]
4	Kevin J. Dalton	[7]
5	Christopher R. Dance	[7]
6	R. Derom	[7]
7	Arnaud Doucet	[8] [12]
8	Frank Fallside	[1] [2]
9	João F. G. de Freitas	[6] [8] [10] [11] [12] [13]
10	Andrew H. Gee	[6] [8] [11] [12] [13]
11	Sean B. Holden	[3] [5]
12	Visakan Kadirkamanathan	[1]
13	Neil D. Lawrence	[16] [17]
14	HongYu Li	[20]
15	D. R. Lovell	[7]
16	Marta Milo	[16]
17	Richard W. Prager	[7] [9]
18	Penny Rashbass	[16]
19	Tony Robinson	[10]
20	Peter Rockett	[20]
21	Gaafar M. K. Saleh	[14]
22	Martin J. J. Scott	[9]
23	Gavin Smith	[10]
24	Stephan Soullier	[16]
25	Sujimarn Suwannaroj	[18]
26	Joab Winkler	[17]
27	Si Wu	[15]
28	Yang Zhang	[20]

Colors in the list of coauthors