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This paper introduces a sequentially motivated approach to processing streams of images from datasets with low memory demands. We utilize fuzzy clustering as an incremental dictionary learning scheme and explain how the corresponding membership functions can be subsequently used in encoding features for image patches. We focus on replicating the codebook learning and classification stages from an...
This paper proposes a method which is suitable for the estimation of the probability of occurrence of a syndrome, as a function of the geographical coordinates of the individuals under risk. The data describing the location of syndrome cases over the population suffers a moving-average filtering, and the resulting values are fitted by an RBF network performing a regression. Some contour curves of...
Multi-class classification problem has become a challenging problem in bioinformatics research. The problem becomes more difficult as the number of classes increases. Decomposing the problem into a set of binary problems can be a good solution in some cases. One of the popular approaches is to build a hierarchical tree structure where a binary classifier is used at each node of the tree. This paper...
Despite early success in automatic chord recognition, recent efforts are yielding diminishing returns while basically iterating over the same fundamental approach. Here, we abandon typical conventions and adopt a different perspective of the problem, where several seconds of pitch spectra are classified directly by a convolutional neural network. Using labeled data to train the system in a supervised...
This paper introduces a neural fuzzy network approach for evolving system modeling. The approach uses neofuzzy neurons and a neural fuzzy structure monished with an incremental learning algorithm that includes adaptive feature selection. The feature selection mechanism starts considering one or more input variables from a given set of variables, and decides if a new variable should be added, or if...
This study presents a novel adaptive control based on a neural network for dc -- dc converters. The control method is required to adapt to changes of conditions to obtain high performance dc -- dc converters. In this study, the neural network control is adopted to improve the transient response of dc -- dc converters. It woks in coordination with a conventional PID control to realize a high adaptive...
Ensembles of neural networks have been the focus of extensive studies over the past two decades. Effectively encouraging diversity remains a key element in yielding improved performance from such ensembles. Negatively correlated learning (NCL) has emerged as a promising framework for concurrently training an ensemble of learners while emphasizing the cooperation among them. The NCL methodology relies...
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