In this paper, an evaluation using various training data sets for discrimination of dysmorphic facial features with distinctive information will be presented. We utilize Gabor Wavelet Transform (GWT) as feature extractor, K-Nearest Neighbor (K-NN) and Support Vector Machines (SVM) as statistical classifiers. We analyzed the classification accuracy according to increasing dimension of training d...

one of the most important problems in geodesy is the unification of height datum. generally in geodesy; there are two types of height systems, the geometrical height based on ellipsoid and the physical height based on gravity-defined surface (zhang et al, 2009).local height datum is determined according to mean sea level (msl). in regarding to mismatch of mean sea level and geoid, on the one ha...

Many practical engineering applications require the usage of accurate automatic decision systems, usually operating under tight computational constraints. Support Vector Machines (SVMs) endowed with a Radial Basis Function (RBF) as kernel are broadly accepted as the current state of the art for decision problems, but require cross-validation to select the free parameters, which is computational...

The image-based plant identification challenge was focused on tree, herbs and ferns species identification based on different types of images. The aim of the task was to produce relevant species for each observation of a plant of the test dataset. We have elaborated a viewpoints combined classification method for this challenge. We have applied dense SIFT for feature detection and description; ...

We construct genRBF kernel, which generalizes the classical Gaussian RBF kernel to the case of incomplete data. We model the uncertainty contained in missing attributes making use of data distribution and associate every point with a conditional probability density function. This allows to embed incomplete data into the function space and to define a kernel between two missing data points based...

Breast cancer is an all too common disease in women, making how to effectively predict it an active research problem. A number of statistical and machine learning techniques have been employed to develop various breast cancer prediction models. Among them, support vector machines (SVM) have been shown to outperform many related techniques. To construct the SVM classifier, it is first necessary ...

Commercial detection in news broadcast videos involves judicious selection of meaningful audio-visual feature combinations and efficient classifiers. And, this problem becomes much simpler if these combinations can be learned from the data. To this end, we propose an Multiple Kernel Learning based method for boosting successful kernel functions while ignoring the irrelevant ones. We adopt a int...

Composite neural networks consist of multilayer networks, in which each layer may use different models of neurons: the classical sigmoidal neuron, the kernel neuron (like radial basis function neurons), the logical neuron, and so on. This section is devoted to supervised composite neural networks and contains three main parts. The first is focused on radial basis function (RBF) networks, as int...

In this communication, we analyze several regularized types of Radial Basis Function (RBF) Networks for crop classification using hyperspectral images. We compare the regularized RBF neural network with Support Vector Machines (SVM) using the RBF kernel, and AdaBoost Regularized (ABR) algorithm using RBF bases, in terms of accuracy and robustness. Several scenarios of increasing input space dim...

In this paper, a supervised technique for training radial basis function (RBF) neural network classifiers is proposed. Such a technique, unlike traditional ones, considers the class memberships of training samples to select the centers and widths of the kernel functions associated with the hidden neurons of an RBF network. The result is twofold: a significant reduction in the overall classifica...