Mahesan Niranjan

Taihai Chen, Evangelos B. Mazomenos, Koushik Maharatna, Srinandan Dasmahapatra, Mahesan Niranjan: Design of a Low-Power On-Body ECG Classifier for Remote Cardiovascular Monitoring Systems. IEEE J. Emerg. Sel. Topics Circuits Syst. 3(1): 75-85 (2013)

Piyushkumar A. Mundra, Jie Zheng, Mahesan Niranjan, Roy E. Welsch, Jagath C. Rajapakse: Inferring Time-Delayed Gene Regulatory Networks Using Cross-Correlation and Sparse Regression. ISBRA 2013: 64-75

Wei Liu, Mahesan Niranjan: Gaussian process modelling for bicoid mRNA regulation in spatio-temporal Bicoid profile. Bioinformatics 28(3): 366-372 (2012)

Xin Liu, Mahesan Niranjan: State and parameter estimation of the heat shock response system using Kalman and particle filters. Bioinformatics 28(11): 1501-1507 (2012)

Ke Yuan, Mark Girolami, Mahesan Niranjan: Markov Chain Monte Carlo Methods for State-Space Models with Point Process Observations. Neural Computation 24(6): 1462-1486 (2012)

Amirthalingam Ramanan, Mahesan Niranjan: A Review of Codebook Models in Patch-Based Visual Object Recognition. Signal Processing Systems 68(3): 333-352 (2012)

Chathurika Dharmagunawardhana, Sasan Mahmoodi, Michael Bennet, Mahesan Niranjan: Unsupervised Texture Segmentation using Active Contours and Local Distributions of Gaussian Markov Random Field Parameters. BMVC 2012: 1-11

Kaya Kuru, Mahesan Niranjan, Yusuf Tunca: Establishment of a Diagnostic Decision Support System in Genetic Dysmorphology. ICMLA (2) 2012: 164-169

Taihai Chen, Evangelos B. Mazomenos, Koushik Maharatna, Srinandan Dasmahapatra, Mahesan Niranjan: On the Trade-Off of Accuracy and Computational Complexity for Classifying Normal and Abnormal ECG in Remote CVD Monitoring Systems. SiPS 2012: 37-42

Yizhao Ni, Craig Saunders, Sándor Szedmák, Mahesan Niranjan: Exploitation of Machine Learning Techniques in Modelling Phrase Movements for Machine Translation. Journal of Machine Learning Research 12: 1-30 (2011)

Andrew Zammit Mangion, Ke Yuan, Visakan Kadirkamanathan, Mahesan Niranjan, Guido Sanguinetti: Online Variational Inference for State-Space Models with Point-Process Observations. Neural Computation 23(8): 1967-1999 (2011)

Salih Tuna, Mahesan Niranjan: Reducing the algorithmic variability in transcriptome-based inference. Bioinformatics 26(9): 1185-1191 (2010)

Ivan Markovsky, Mahesan Niranjan: Approximate low-rank factorization with structured factors. Computational Statistics & Data Analysis 54(12): 3411-3420 (2010)

Yizhao Ni, Craig Saunders, Sándor Szedmák, Mahesan Niranjan: The application of structured learning in natural language processing. Machine Translation 24(2): 71-85 (2010)

Ke Yuan, Mahesan Niranjan: Estimating a State-Space Model from Point Process Observations: A Note on Convergence. Neural Computation 22(8): 1993-2001 (2010)

Weichao Xu, Y. S. Hung, Mahesan Niranjan, Minfen Shen: Asymptotic mean and variance of Gini correlation for bivariate normal samples. IEEE Transactions on Signal Processing 58(2): 522-534 (2010)

Salih Tuna, Mahesan Niranjan: Inference from Low Precision Transcriptome Data Representation. Signal Processing Systems 58(3): 267-279 (2010)

Yizhao Ni, Mahesan Niranjan: Exploiting Long-Range Dependencies in Protein beta-Sheet Secondary Structure Prediction. PRIB 2010: 349-357

Yizhao Ni, Craig Saunders, Sándor Szedmák, Mahesan Niranjan: Handling phrase reorderings for machine translation. ACL/IJCNLP (Short Papers) 2009: 241-244

C. Q. Chang, Y. S. Hung, Mahesan Niranjan: Modelling uncertainty in transcriptome measurements enhances network component analysis of yeast metabolic cycle. ICASSP 2009: 1769-1772

Bassam Farran, Amirthalingam Ramanan, Mahesan Niranjan: Sequential Hierarchical Pattern Clustering. PRIB 2009: 79-88

Salih Tuna, Mahesan Niranjan: Cross-Platform Analysis with Binarized Gene Expression Data. PRIB 2009: 439-449

Visakan Kadirkamanathan, Guido Sanguinetti, Mark A. Girolami, Mahesan Niranjan, Josselin Noirel (Eds.): Pattern Recognition in Bioinformatics, 4th IAPR International Conference, PRIB 2009, Sheffield, UK, September 7-9, 2009. Proceedings. Lecture Notes in Computer Science 5780, Springer 2009, isbn 978-3-642-04030-6

Renata da Silva Camargo, Mahesan Niranjan: Mining Protein Database using Machine Learning Techniques. J. Integrative Bioinformatics 5(2) (2008)

Sujimarn Suwannaroj, Mahesan Niranjan: Enhancing Automatic Construction of Gene Subnetworks by Integrating Multiple Sources of Information. Signal Processing Systems 50(3): 331-340 (2008)

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

Anastasia Samsonova, Mahesan Niranjan, Steven Russell, Alvis Brazma: Prediction of Gene Expression in Embryonic Structures of Drosophila melanogaster. PLoS Computational Biology 3(7) (2007)

Joab Winkler, Mahesan Niranjan, Neil D. Lawrence (Eds.): Deterministic and Statistical Methods in Machine Learning, First International Workshop, Sheffield, UK, September 7-10, 2004, Revised Lectures. Lecture Notes in Computer Science 3635, Springer 2005, isbn 3-540-29073-7

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): 518-526 (2004)

Si Wu, Danmei Chen, Mahesan Niranjan, Shun-ichi Amari: Sequential Bayesian Decoding with a Population of Neurons. Neural Computation 15(5): 993-1012 (2003)

Gaafar M. K. Saleh, Mahesan Niranjan: Speech enhancement using a Bayesian evidence approach. Computer Speech & Language 15(2): 101-125 (2001)

Konstantinos Koumpis, Steve Renals, Mahesan Niranjan: Extractive summarization of voicemail using lexical and prosodic feature subset selection. INTERSPEECH 2001: 2377-2380

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)

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)

David G. Melvin, Mahesan Niranjan, Richard W. Prager, Andrew K. Trull, Vikki F. Hughes: Neuro-computing versus linear statistical techniques applied to liver transplant monitoring: a comparative study. IEEE Trans. Biomed. Engineering 47(8): 1036-1043 (2000)

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)

Nathan Smith, Mahesan Niranjan: Data-dependent kernels in svm classification of speech patterns. INTERSPEECH 2000: 297-300

Klaus Reinhard, Mahesan Niranjan: Diphone subspace models for phone-based HMM complementation. EUROSPEECH 1999

Gavin Smith, João F. G. de Freitas, Tony Robinson, Mahesan Niranjan: Speech Modelling Using Subspace and EM Techniques. NIPS 1999: 796-802

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)

Martin J. J. Scott, Mahesan Niranjan, Richard W. Prager: Realisable Classifiers: Improving Operating Performance on Variable Cost Problems. BMVC 1998: 1-10

João F. G. de Freitas, Sue E. Johnson, Mahesan Niranjan, Andrew H. Gee: Global optimisation of neural network models via sequential sampling-importance resampling. ICSLP 1998

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

Sean B. Holden, Mahesan Niranjan: Average-Case Learning Curves for Radial Basis Function Networks. Neural Computation 9(2): 441-460 (1997)

João F. G. de Freitas, Mahesan Niranjan, Andrew H. Gee: Regularisation in Sequential Learning Algorithms. NIPS 1997

Mahesan Niranjan: Sequential Tracking in Pricing Financial Options using Model Based and Neural Network Approaches. NIPS 1996: 960-966

Sean B. Holden, Mahesan Niranjan: On the practical applicability of VC dimension bounds. Neural Computation 7(6): 1265-1288 (1995)

Sean B. Holden, Mahesan Niranjan: On the statistical physics of radial basis function networks. Neural Processing Letters 2(4): 16-19 (1995)

Gaafar M. K. Saleh, Mahesan Niranjan, W. J. Fitzgerald: The use of maximum a posteriori parameters in linear prediction of speech. EUROSPEECH 1995

Lizhong Wu, Mahesan Niranjan, Frank Fallside: Fully vector-quantized neural network-based code-excited nonlinear predictive speech coding. IEEE Transactions on Speech and Audio Processing 2(4): 482-489 (1994)

Visakan Kadirkamanathan, Mahesan Niranjan: A Function Estimation Approach to Sequential Learning with Neural Networks. Neural Computation 5(6): 954-975 (1993)

Mahesan Niranjan, Frank Fallside: Speech Feature Extraction Using Neural Networks. EURASIP Workshop 1990: 197-204

Visakan Kadirkamanathan, Mahesan Niranjan, Frank Fallside: Sequential Adaptation of Radial Basis Function Networks. NIPS 1990: 721-727

Mahesan Niranjan, Frank Fallside: On modelling the dynamics of speech patterns. ECST 1987: 1071-1074