We have developed a novel neural classifier LImited Receptive Area (LIRA) for the image
recognition. The classifier LIRA contains three neuron layers: sensor, associative and output …

A feature extractor and neural classifier for image recognition systems are proposed. The
proposed feature extractor is based on the concept of random local descriptors (RLDs). It is …

A novel and generic approach for image classification is presented. The method operates
directly on pixel values and does not require feature extraction. It combines a simple local …

In this paper we present two methods to create multiple classifier systems based on an initial
transformation of the original features to the binary domain and subsequent decompositions …

In this paper, we evaluate 7 machine learning algorithms for image classification including
our recent approach that combines building of ensembles of extremely randomized trees …

In this paper, limited receptive area neural classifiers are described which are based upon
Rosenblatt's perceptron. These networks can be used for both binary and gray-level images …

The new neural classifier for the handwritten digit recognition is proposed. The classifier is
based on the Permutative Coding technique. This coding technique is derived from the …

In this master thesis, we present a new framework for the automatic detection, recognition
and learning of human faces with random forests. The principal motivation for our work is …

MNIST database serves for comparison of different methods of handwritten digit recognition.
There are many data related to different classifier recognition rates among which our neural …

Ultra-wideband (UWB) is a physical (PHY) layer technology that promises high transmission
rates, as well as high resistance to noise and multipath effects. However, the impulse-based …