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Identification of Driver's Drowsiness Using Driving Information and EEG
Jiřina, Marcel ; Novotný, S. ; Bouchner, P.
This report summarizes the first results with identification of sleepy state in drivers. The driving information as the deviation from the centerline of road and the steering wheel position as well as two-point eeg was used. The process consists of preprocessing data, in fact a transformation into form proper for classification, and a classification into one of two classes, wakefulness and drowsiness. Results show that it is possible to distinguish these two states with relatively large error, which possibly can be tackled by the use of proper methodology.
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Klasifikátor založený na inverzních hodnotách indexů II. teorie a příloha
Jiřina, Marcel ; Jiřina jr., M.
A theory of a new method for the classification of data into classes is presented. The method is based on the sum of reciprocals of neighbors' indexes. We show that neighbors' indexes are in close relation to the approximate polynomial transform of the neighbors' distances. The sum of the reciprocals of indexes for all neighbors forms truncated harmonic series due to a finite number of its elements. For the neighbors of one class there is a sum of the selected elements of this truncated series. It is proved that the ratio of these sums gives just the probability that the point to be classified - the query point - is of that class.
Fulltext: content.csg -  PDF
Plný tet: v1041-08 - PDF
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Klasifikátor založený na inverzních hodnotách indexů
Jiřina, Marcel ; Jiřina jr., M.
A new method for the classification of data into classes is presented. The method is based on the sum of reciprocals of neighbors' indexes. We show that neighbors' indexes are in close relation to the polynomial transform of the neighbors' distances. The sum of the reciprocals of indexes for all neighbors forms truncated harmonic series due to a finite number of its elements. For the neighbors of one class there is a sum of the selected elements of this truncated series. It is proved that the ratio of these sums gives just the probability that the point to be classified -- the query point -- is of that class. The classification ability is demonstrated on real-life data from the Machine Learning Repository and the results are compared with published results obtained through other methods.
Fulltext: content.csg - PDF
Plný tet: v1034-08 - PDF
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Metoda váhované metriky s nehladkým procesem učení
Jiřina, Marcel ; Jiřina jr., M.
A new approach to the Learning Weighted Metrics method for optimized classification of data with 1-NN rule Vidal is proposed. New approach is based on application of updating rule similar to one of Madaline neural network, and on dynamic optimization of the step size similar to Runge's method of half step. A short theory is given and the classification ability is demonstrated.
Fulltext: content.csg - PDF
Plný tet: v1026-08 - PDF
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Genetická selekce a klonování u metody GMDH-MIA
Jiřina, Marcel ; Jiřina jr., M.
The GMDH MIA algorithm is modified by the use of selection procedure from genetic algorithms and including cloning of the best neurons generated. The selection procedure finds parents for a new neuron among already existing neurons according to fitness and with some probability also from network inputs. The essence of cloning is slight modification of parameters of copies of the best neuron, i.e. neuron with the largest fitness. The genetically modified GMDH network with cloning (GMC-GMDH) can outperform other powerful methods. It is demonstrated on some tasks from Machine Learning Repository.
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