Searching for authors named "Aristidis Likas" – sorted by Relevance.
25 documents found, showing 1 through 10.
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Reinforcement Learning Using The Stochastic Fuzzy Min-Max Neural Network
- …The fuzzy min-max neural network constitutes a neural architecture that is based on hyperbox fuzzy sets and can be incrementally trained by appropriately adjusting the number of hyperboxes and their corresponding volumes. An extension to this network has been proposed recently, that is based on t…
- Cited by 1 (0 self) – Add To MetaCart
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A Reinforcement Learning Approach to On-line Clustering
- …A general technique is proposed for embedding on-line clustering algorithms based on competitive learning in a reinforcement learning framework. The basic idea is that the clustering system can be viewed as a reinforcement learning system that learns through reinforcements to follow the clustering s…
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Training the Random Neural Network Using Quasi-Newton Methods
- …Training in the Random Neural Network is generally specified as the minimization of an appropriate error function with respect to the parameters of the network (weights corresponding to positive and negative connections). We propose here a technique for error minimization that is based on the use of…
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An Investigation of the Analogy between the Random Network and the Hopfield Network
- …In recent publications, a new neural network model, called the random network, has been introduced, in which excitation and inhibition are represented by positive and negative signals. It has been shown that, under markovian assumptions, this model has a product form solution, and several issues ha…
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Incremental Mixture Learning for Clustering Discrete Data
- …Abstract. This paper elaborates on an efficient approach for clustering discrete data by incrementally building multinomial mixture models through likelihood maximization using the Expectation-Maximization (EM) algorithm. The method adds sequentially at each step a new multinomial component to a mix…
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A Kurtosis-Based Dynamic Approach to Gaussian Mixture Modeling
- …We address the problem of probability density function estimation using a Gaussian mixture model updated with the expectation-maximization (EM) algorithm. To deal with the case of an unknown number of mixing kernels, we define a new measure for Gaussian mixtures, called total kurtosis, which is base…
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Group Updates and Multiscaling: An Efficient Neural Network Approach to Combinatorial Optimization
- …A multiscale method is described in the context of binary Hopfield--type neural networks. The appropriateness of the proposed technique for solving several classes of optimization problems is established by means of the notion of group update which is introduced here and investigated in relation to …
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A greedy EM algorithm for Gaussian mixture learning
- …Learning a Gaussian mixture with a local algorithm like EM can be difficult because (i) the true number of mixing components is usually unknown, (ii) there is no generally accepted method for parameter initialization, and (iii) the algorithm can get stuck in one of the many local maxima of the likel…
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Multivariate Gaussian mixture modeling with unknown number of components
- …We are dealing with the problem of Gaussian mixture density estimation with...…
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Greedy Mixture Learning for Multiple Motif Discovery in Biological Sequences
- …Motivation: This paper studies the problem of discovering subsequences, known as motifs, that are common to a given collection of related biosequences, by proposing agreedy algorithm for learning a mixture of motifs model through likelihood maximization. The approach adds sequentially a new motif to…
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