Bipolar convergence in genetic algorithm for multimodal optimization.
Building adaptive tests using Bayesian networks
We propose a framework for building decision strategies using Bayesian network models and discuss its application to adaptive testing. Dynamic programming and algorithm are used to find optimal adaptive tests. The proposed algorithm is based on a new admissible heuristic function.
Cascading classifiers
We propose a multistage recognition method built as a cascade of a linear parametric model and a -nearest neighbor (-NN) nonparametric classifier. The linear model learns a “rule” and the -NN learns the “exceptions” rejected by the “rule.” Because the rule-learner handles a large percentage of the examples using a simple and general rule, only a small subset of the training set is stored as exceptions during training. Similarly during testing, most patterns are handled by the rule -learner and...
Coevolutionary genetic algorithms for establishing Nash equilibrium in symmetric Cournot games.
Comment on “Convergence properties of adaptive threshold elements...”
Comparison of supervised learning methods for spike time coding in spiking neural networks
In this review we focus our attention on supervised learning methods for spike time coding in Spiking Neural Networks (SNNs). This study is motivated by recent experimental results regarding information coding in biological neural systems, which suggest that precise timing of individual spikes may be essential for efficient computation in the brain. We are concerned with the fundamental question: What paradigms of neural temporal coding can be implemented with the recent learning methods? In order...
Completing an uncertainty criterion of classification.
We present a variation of a method of classification based in uncertainty on credal set. Similarly to its origin it use the imprecise Dirichlet model to create the credal set and the same uncertainty measures. It take into account sets of two variables to reduce the uncertainty and to seek the direct relations between the variables in the data base and the variable to be classified. The success are equivalent to the success of the first method except in those where there are a direct relations between...
Computational exploration of the biological basis of Black-Scholes expected utility function.
Conception de séquences de séparation par un algorithme génétique
Conceptual base of feature selection consulting system
The paper briefly reviews recent advances in the methodology of feature selection (FS) and the conceptual base of a consulting system for solving FS problems. The reasons for designing a kind of expert or consulting system which would guide a less experienced user are outlined. The paper also attempts to provide a guideline which approach to choose with respect to the extent of a priori knowledge of the problem. The methods discussed here form the core of the software package being developed for...
Construction of nonlinear discrimination function based on the MDL criterion
Although a nonlinear discrimination function may be superior to linear or quadratic classifiers, it is difficult to construct such a function. In this paper, we propose a method to construct a nonlinear discrimination function using Legendre polynomials. The selection of an optimal set of Legendre polynomials is determined by the MDL (Minimum Description Length) criterion. Results using many real data show the effectiveness of this method.
Controllability, applications, and numerical simulations of cellular neural networks.
Controllability of time-varying cellular neural networks.
Convergence analysis for principal component flows
A common framework for analyzing the global convergence of several flows for principal component analysis is developed. It is shown that flows proposed by Brockett, Oja, Xu and others are all gradient flows and the global convergence of these flows to single equilibrium points is established. The signature of the Hessian at each critical point is determined.
Convergence of an online split-complex gradient algorithm for complex-valued neural networks.
Convergence of batch split-complex backpropagation algorithm for complex-valued neural networks.
Convergence properties of adaptive threshold elements in respect to application and implementation
Correlation-based feature selection strategy in classification problems
In classification problems, the issue of high dimensionality, of data is often considered important. To lower data dimensionality, feature selection methods are often employed. To select a set of features that will span a representation space that is as good as possible for the classification task, one must take into consideration possible interdependencies between the features. As a trade-off between the complexity of the selection process and the quality of the selected feature set, a pairwise...
Could a combinatorial optimization problem be solved by a differential equation?
El Problema del Viajante puede plantearse a partir del modelo de red neuronal continuo de Hopfield, que determina una solución de equilibrio para una ecuación diferencial con parámetros desconocidos. En el artículo se detalla el procedimiento de determinación de dichos parámetros con el fin de asegurar que la solución de la ecuación diferencial proporcione soluciones válidas para el Problema del Viajante.
Cross-task code reuse in genetic programming applied to visual learning
We propose a method that enables effective code reuse between evolutionary runs that solve a set of related visual learning tasks. We start with introducing a visual learning approach that uses genetic programming individuals to recognize objects. The process of recognition is generative, i.e., requires the learner to restore the shape of the processed object. This method is extended with a code reuse mechanism by introducing a crossbreeding operator that allows importing the genetic material from...