Two chemical substructure searching algorithms, the relaxation algorithm and the set reduction algorithm, are introduced and described. Transputer based serial implementations of both are compared for performance;the ...
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Picking up the characteristic values of traditional Chinese medicine sphygmograms using the system identification approach is considered in this paper. The successive approximation method to determine the pulse graph&...
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Picking up the characteristic values of traditional Chinese medicine sphygmograms using the system identification approach is considered in this paper. The successive approximation method to determine the pulse graph's estimated parameters, similar to the relaxation algorithm, is investigated. Several typical pulses, the chronic nephritis and acute urethritis abnormal pulse graph before and after cure, some cases of the finger plethysmograms of the healthy and the hyper-tensive before and after exercise, as well as the sphygmograms of about 50 pregnant women, are given. These results indicate, that by identifying the pulse's type, diagnosing a disease and forecasting a fetus sex is possible.
A system is presented which, when given a list of points on the plane, will find a good figure to approximately pass through the points. Some of the thirteen figures considered are the line segment, circle, parallelog...
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A system is presented which, when given a list of points on the plane, will find a good figure to approximately pass through the points. Some of the thirteen figures considered are the line segment, circle, parallelogram, and equilateral triangle. The system searches a disjunctive (or) goal tree. We are performing research needed in the development of a robot manipulator system. Object recognition is a very important part of the system. Although the robot will be sensing three-dimensional objects from tactile or other sensors, there are reasons for first treating the two-dimensional case.
In two-class pattern recognition, it is a standard technique to have an algorithm finding hyperplanes which separates the two classes in a linearly separable training set. The traditional methods find a hyperplane whi...
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In two-class pattern recognition, it is a standard technique to have an algorithm finding hyperplanes which separates the two classes in a linearly separable training set. The traditional methods find a hyperplane which separates all points in one class from all points in the other, but such a hyperplane is not necessarily centered in the empty space between the two classes. Since a central hyperplane does not favor one class or the other, it should have a lower error rate in classifying new points and is therefore better than a noncentral hyperplane. Six algorithms for finding central hyperplanes are tested on three data sets. Although frequently used in practice, the modified relaxation algorithm is very poor. Three algorithms which are defined in the paper are found to be quite good. [ABSTRACT FROM AUTHOR]
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