[Comparative efficiency of algorithms based on support vector machines for binary classification].
Methods of construction of support vector machines require no further a priori infoimation and provide big data processing, what is especially important for various problems in computational biology. The question of the quality of learning algorithms is considered. The main algorithms of support vector machines for binary classification are reviewed and they were comparatively explored for their efficiencies. The critical analysis of the results of this study revealed the most effective support-vector-classifiers. The description of the recommended algorithms, sufficient for their practical implementation, is presented.
This paper describes a layer structured system suitable for pattern recognition which operates similar to the afferent nervous system of vertebrates. The ``system theory of homogeneous layers'' has been developed to describe signal transmission and signal processing between neuronal layers. Feature extraction in the sense of spatial filtering is performed by such a layered system with a few hierarchical stages. The last stage contains adaptive coupling which is adjusted by a learning process. The system has been simulated with a computer and parts of it with a coherent light arrangement. Its performance im recognizing handprinted characters (alphanumerics) is highly satisfactory and corresponds approximately to the human capability for this task.