Cooling schedules for optimal annealing
Mathematics of Operations Research
Threshold circuits of bounded depth
Journal of Computer and System Sciences
Multicategory Classification by Support Vector Machines
Computational Optimization and Applications - Special issue on computational optimization—a tribute to Olvi Mangasarian, part I
Local Search in Combinatorial Optimization
Local Search in Combinatorial Optimization
Boosting the margin: A new explanation for the effectiveness of voting methods
ICML '97 Proceedings of the Fourteenth International Conference on Machine Learning
Many-Layered versus Few-Layered Learning TITLE2:
Many-Layered versus Few-Layered Learning TITLE2:
Logarithmic simulated annealing for X-ray diagnosis
Artificial Intelligence in Medicine
ICANN '02 Proceedings of the International Conference on Artificial Neural Networks
Binary and multicategory classification accuracy of the LSA machine
ICCMSE '03 Proceedings of the international conference on Computational methods in sciences and engineering
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We present a stochastic algorithm that computes threshold circuits designed to discriminate between two classes of computed tomography (CT) images. The algorithm employs a partition of training examples into several classes according to the average grey scale value of images. For each class, a sub-circuit is computed, where the first layer of the sub-circuit is calculated by a new combination of the Perceptron algorithm with a special type of simulated annealing. The algorithm is evaluated for the case of liver tissue classification. A depth-five threshold circuit (with pre-processing: depth-seven) is calculated from 400 positive (abnormal findings) and 400 negative (normal liver tissue) examples. The examples are of size n=14,161 (119 x119) with an 8 bit grey scale. On test sets of 100 positive and 100 negative examples (all different from the learning set) we obtain a correct classification close to 99%. The total sequential run-time to compute a depth-five circuit is about 75h up to 230h on a SUN Ultra 5/360 workstation, depending on the width of the threshold circuit at depth-three. In our computational experiments, the depth-five circuits were calculated from three simultaneous runs for depth-four circuits. The classification of a single image is performed within a few seconds.