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This paper proposes a hybrid neural network model using a possible combination of different transfer projection functions (sigmoidal unit, SU, product unit, PU) and kernel functions (radial basis function, RBF) in the hidden layer of a feed-forward neural network. An evolutionary algorithm is adapted to this model and applied for learning the architecture, weights and node typology. Three diffe...

Iris recognition is one of commonly employed biometric for personal recognition. In this paper, Single Value Decomposition (SVD), Automatic Feature Extraction (AFE), Principal Component Analysis (PCA) and Independent Component Analysis (ICA) are used to extract the iris feature from a pattern named IrisPattern based on the iris image. The IrisPatterns are classified using a Feedforward Backprop...

Meteorological volumetric data are used to detect thunderstorms that are the cause of most of the summer severe weathers. There are systems that may convert the volumetric data into a set of derived products. Based on these derived features, this work compares three classifiers to determine which approach will best classify a storm cell data set coming from Environment Canada. The criterion for...

Unmanned Aerial Vehicles (UAVs) are widely used and meet many demands in military civilian fields. With the continuous enrichment extensive expansion of application scenarios, safety UAVs is constantly being challenged. To address this challenge, we propose algorithms to detect anomalous data collected from drones improve drone safety. We deployed a one-class kernel extreme learning machine (OC...

A structural similarity kernel is presented in this paper for SVM learning, especially for learning with imbalanced datasets. Kernels in SVM are usually pairwise, comparing the similarity of two examples only using their feature vectors. By building a neighborhood graph (kNN graph) using the training examples, we propose to utilize the similarity of linking structures of two nodes as an additio...

Extreme learning machines are fast models which almost compare to standard SVMs in terms of accuracy, but are much faster. However, they optimise a sum of squared errors whereas SVMs are maximum-margin classifiers. This paper proposes to merge both approaches by defining a new kernel. This kernel is computed by the first layer of an extreme learning machine and used to train a SVM. Experiments ...

The Support Vector Machine (SVM) has emerged in recent years as a popular approach to the classification of data. One problem that faces the user of an SVM is how to choose a kernel and the specific parameters for that kernel. Applications of an SVM therefore require a search for the optimum settings for a particular problem. This paper proposes a classification technique, which we call the Gen...

One of the central problems in the study of Support vector machine (SVM) is kernel selection, that’s based essentially on the problem of choosing a kernel function for a particular task and dataset. By contradiction to other machine learning algorithms, SVM focuses on maximizing the generalisation ability, which depends on the empirical risk and the complexity of the machine. In the following p...