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The following topics are dealt with: learning algorithm; unsupervised learning; ensembles and meta learning; computational intelligence; bioinformatics; evolutionary multi-objective optimization; fuzzy systems; and adaptive intelligent control.
By exploiting the properties of superposition and entanglement found in quantum systems Quantum Computation has been applied to the design of algorithms considerably more efficient than the known classical ones. Known examples are the Shor's factoring algorithm and the Grover's search algorithm. This paper investigates the possibility of employing Quantum Computing techniques to the design of learning...
We show that the learning sample complexity of a sigmoidal neural network constructed by Sontag (1992) required to achieve a given misclassification error under a fixed purely atomic distribution can grow arbitrarily fast: for any prescribed rate of growth there is an input distribution having this rate as the sample complexity, and the bound is asymptotically tight. The rate can be super exponential,...
Simplified Silhouette Filter (SSF) is a recently introduced feature selection method that automatically estimates the number of features to be selected. To do so, a sampling strategy is combined with a clustering algorithm that seeks clusters of correlated (potentially redundant) features. It is well known that the choice of a similarity measure may have great impact in clustering results. As a consequence,...
This paper presents a novel technique to estimate the number of hidden neurons of an MLP classifier. The proposed approach consists in the post-training application of SVD/PCA to the back propagated error and local gradient matrices associated with the hidden neurons. The number of hidden neurons is then set to the number of relevant singular values or eigenvalues of the involved matrices. Computer...
This paper presents a new probabilistic neural network model, called IPNN (for Incremental Probabilistic Neural Network), which is able to learn continuously probability distributions from data flows. The proposed model is inspired in the Specht's general regression neural network, but have several improvements which makes it more suitable to be used in on-line and robotic tasks. Moreover, IPNN is...
This paper proposes the application of Self-Organizing Maps (SOM) in the construction of an Information Retrieval System (IRS) to the Digital Library of Theses and Dissertations at Federal University of Pernambuco (BDTD-UFPE). The hypothesis is that the trained SOM and its graphical representation can help the user to obtain a general view of topics discussed in the document collection and also to...
The design and test of a two-stage PCA+SOM methodology targeting applications on images database are presented and the result of the SOM map is analyzed by reconstructing the prototypes (codebook) of the map in terms of concrete images in the input space. This visual analysis allows us to interpret which features were used by the SOM algorithm to create a self-organizing map. Several approaches in...
Based on the Theory of Neuronal Group Selection (TNGS), proposed by Edelman, a network composed of one hundred Izhikevich spiking neurons is analyzed. In this study, a genetic algorithm is used to estimate the Izhikevich neuron model parameters in order to enable the self-organization of a neural network into a cluster of tightly coupled neural cells which fire and oscillate in synchrony at a predefined...
No clustering algorithm is guaranteed to find actual groups in any dataset. Thus, the selection of the most suitable clustering algorithm to be applied to a given dataset is not easy. To deal with this problem, one can apply various clustering algorithms to the dataset, generating a set of partitions (solutions). Next, one can choose the best partition generated, according to a given validation measure...
Biometric systems automatically recognize individuals based on their physiological and/or behavioral characteristics like fingerprint, face, hand-geometry, iris, retina, palmprint, voice, gait, signature, and keystroke dynamics. These systems offer several advantages over traditional forms of identity protection (e.g. password-based). Nevertheless, many biometric characteristics are immutable, resulting...
The use of feature selection methods in ensemble systems has been shown to be efficient, since it reduces the dimensionality while increases the diversity among the individual classifiers of these systems. The ReinSel method, a simple reinforcement-based process, for instance, has been proposed to select feature for the individual classifiers of an ensemble system. This method distributes the attributes...
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