On functional approximation with normalized Gaussian units
Neural Computation
Regularization theory and neural networks architectures
Neural Computation
The nature of statistical learning theory
The nature of statistical learning theory
Learning and generalization in radial basis function networks
Neural Computation
Self-organization using Potts models
Neural Networks
Faithful representation of separable distributions
Neural Computation
An equivalence between sparse approximation and support vector machines
Neural Computation
A Deterministic Annealing Approach for Parsimonious Design of Piecewise Regression Models
IEEE Transactions on Pattern Analysis and Machine Intelligence
Fast training of support vector machines using sequential minimal optimization
Advances in kernel methods
Using support vector machines for time series prediction
Advances in kernel methods
Machine Learning
Constrained Clustering as an Optimization Method
IEEE Transactions on Pattern Analysis and Machine Intelligence
Adaptive structures with algebraic loops
IEEE Transactions on Neural Networks
A mixed integer optimisation model for data classification
Computers and Industrial Engineering
A use of DEA-DA to measure importance of R&D expenditure in Japanese information technology industry
Decision Support Systems
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Natural discriminant analysis based on interactive Potts models is developed in this work. A generative model composed of piece-wise multivariate gaussian distributions is used to characterize the input space, exploring the embedded clustering and mixing structures and developing proper internal representations of input parameters. The maximization of a log-likelihood function measuring the fitness of all input parameters to the generative model, and the minimization of a design cost summing up square errors between posterior outputs and desired outputs constitutes a mathematical framework for discriminant analysis. We apply a hybrid of the mean-field annealing and the gradient-descent methods to the optimization of this framework and obtain multiple sets of interactive dynamics, which realize coupled Potts models for discriminant analysis. The new learning process is a whole process of component analysis, clustering analysis, and labeling analysis. Its major improvement compared to the radial basis function and the support vector machine is described by using some artificial examples and a real-world application to breast cancer diagnosis.