The aims of the paper are multifold, to propose a new method to determine a suitable value of the bias corresponding to the soft margin SVM classifier and to experimentally evaluate the quality of the found value against one of the standard expression of the bias computed in terms of the support...

The main aim of the paper is to briefly investigate the most significant topics of the currently used methodologies of solving and implementing SVM-based classifier. Following a brief introductory part, the basics of linear SVM and non-linear SVM models are briefly exposed in the next two...

The restoration techniques are usually oriented toward modeling the type of degradation in order to infer the inverse process for recovering the given image. This approach usually involves the option for a criterion to numerically evaluate the quality of the resulted image and consequently the...

So far, considerable research efforts have been invested in the are of using statistical methods for image processing purposes yielding to a significant amount of models that aim to improve as much as possible the still existing and currently used processing techniques, some of them being based...

Kernel methods and support vector machines have become the most popular learning from examples paradigms. Several areas of application research make use of SVM approaches as for instance hand written character recognition, text categorization, face detection, pharmaceutical data analysis and...

A cluster analysis task has to identify the grouping trends of data, to decide on the sound clusters as well as to validate somehow the resulted structure. The identification of the grouping tendency existing in a data collection assumes the selection of a framework stated in terms of a...

The paper focuses on the potential of principal directions based approaches in signal classification and recognition. In probabilistic models, the classes are represented in terms of multivariate density functions, and an object coming from a certain class is modeled as a random vector whose...

Data mining is an evolving and growing area of research and involves interdisciplinary research and development encompassing diverse domains. In this age of multimedia data exploration, data mining should no longer be restricted to the mining of knowledge from large volumes of high-dimensional...

An important feature of neural networks is the ability they have to learn from their environment, and, through learning to improve performance in some sense. In the following we restrict the development to the problem of feature extracting unsupervised neural networks derived on the base of the...