Introduction to statistical pattern recognition (2nd ed.)
Introduction to statistical pattern recognition (2nd ed.)
The nature of statistical learning theory
The nature of statistical learning theory
Machine learning, neural and statistical classification
Machine learning, neural and statistical classification
Prediction with Gaussian processes: from linear regression to linear prediction and beyond
Learning in graphical models
Least Squares Support Vector Machine Classifiers
Neural Processing Letters
Proximal support vector machine classifiers
Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining
NETLAB: algorithms for pattern recognition
NETLAB: algorithms for pattern recognition
Bayesian Learning for Neural Networks
Bayesian Learning for Neural Networks
Neural Networks for Pattern Recognition
Neural Networks for Pattern Recognition
Pattern Recognition and Neural Networks
Pattern Recognition and Neural Networks
Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond
Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond
Estimating a Kernel Fisher Discriminant in the Presence of Label Noise
ICML '01 Proceedings of the Eighteenth International Conference on Machine Learning
Shrinkage estimator generalizations of Proximal Support Vector Machines
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
Generalized Discriminant Analysis Using a Kernel Approach
Neural Computation
Kernel Maximum a Posteriori Classification with Error Bound Analysis
Neural Information Processing
GEP-Induced Expression Trees as Weak Classifiers
ICDM '08 Proceedings of the 8th industrial conference on Advances in Data Mining: Medical Applications, E-Commerce, Marketing, and Theoretical Aspects
Sparse multinomial kernel discriminant analysis (sMKDA)
Pattern Recognition
Implicit emotional tagging of multimedia using EEG signals and brain computer interface
WSM '09 Proceedings of the first SIGMM workshop on Social media
Proximal support vector machine using local information
Neurocomputing
Multiclass probabilistic kernel discriminant analysis
IJCAI'09 Proceedings of the 21st international jont conference on Artifical intelligence
Determine the Kernel Parameter of KFDA Using a Minimum Search Algorithm
ICIC '07 Proceedings of the 3rd International Conference on Intelligent Computing: Advanced Intelligent Computing Theories and Applications. With Aspects of Artificial Intelligence
The kernelHMM: learning kernel combinations in structured output domains
Proceedings of the 29th DAGM conference on Pattern recognition
On Over-fitting in Model Selection and Subsequent Selection Bias in Performance Evaluation
The Journal of Machine Learning Research
Two ensemble classifiers constructed from GEP-induced expression trees
KES-AMSTA'10 Proceedings of the 4th KES international conference on Agent and multi-agent systems: technologies and applications, Part II
Constructing nonlinear discriminants from multiple data views
ECML PKDD'10 Proceedings of the 2010 European conference on Machine learning and knowledge discovery in databases: Part I
Cellular GEP-induced classifiers
ICCCI'10 Proceedings of the Second international conference on Computational collective intelligence: technologies and applications - Volume PartI
Cellular gene expression programming classifier learning
Transactions on computational collective intelligence V
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In this paper we consider a novel Bayesian interpretation of Fisher's discriminant analysis. We relate Rayleigh's coefficient to a noise model that minimises a cost based on the most probable class centres and that abandons the 'regression to the labels' assumption used by other algorithms. Optimisation of the noise model yields a direction of discrimination equivalent to Fisher's discriminant, and with the incorporation of a prior we can apply Bayes' rule to infer the posterior distribution of the direction of discrimination. Nonetheless, we argue that an additional constraining distribution has to be included if sensible results are to be obtained. Going further, with the use of a Gaussian process prior we show the equivalence of our model to a regularised kernel Fisher's discriminant. A key advantage of our approach is the facility to determine kernel parameters and the regularisation coefficient through the optimisation of the marginal log-likelihood of the data. An added bonus of the new formulation is that it enables us to link the regularisation coefficient with the generalisation error.