Cooling schedules for optimal annealing
Mathematics of Operations Research
Simulated annealing and Boltzmann machines: a stochastic approach to combinatorial optimization and neural computing
The perceptron algorithm is fast for nonmalicious distributions
Neural Computation
Symbolic and Neural Learning Algorithms: An Experimental Comparison
Machine Learning
Learning linear threshold functions in the presence of classification noise
COLT '94 Proceedings of the seventh annual conference on Computational learning theory
Learning linear threshold approximations using perceptrons
Neural Computation
Journal of Complexity - Special issue for the Foundations of Computational Mathematics conference, Rio de Janeiro, Brazil, Jan. 1997
Stochastic simulations of two-dimensional composite packings
Journal of Computational Physics
On Various Cooling Schedules for Simulated Annealing Applied to the Job Shop Problem
RANDOM '98 Proceedings of the Second International Workshop on Randomization and Approximation Techniques in Computer Science
On Logarithmic Simulated Annealing
TCS '00 Proceedings of the International Conference IFIP on Theoretical Computer Science, Exploring New Frontiers of Theoretical Informatics
Learning noisy perceptrons by a perceptron in polynomial time
FOCS '97 Proceedings of the 38th Annual Symposium on Foundations of Computer Science
Perceptron-based learning algorithms
IEEE Transactions on Neural Networks
Depth-Four Threshold Circuits for Computer-Assisted X-ray Diagnosis
AIME '01 Proceedings of the 8th Conference on AI in Medicine in Europe: Artificial Intelligence Medicine
Binary and multicategory classification accuracy of the LSA machine
ICCMSE '03 Proceedings of the international conference on Computational methods in sciences and engineering
Bounded-depth threshold circuits for computer-assisted CT image classification
Artificial Intelligence in Medicine
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We present a new stochastic learning algorithm and first results of computational experiments on fragments of liver CT images. The algorithm is designed to compute a depth-three threshold circuit, where the first layer is calculated by an extension of the Perceptron algorithm by a special type of simulated annealing. The fragments of CT images are of size 119x119 with eight bit grey levels. From 348 positive (focal liver tumours) and 348 negative examples a number of hypotheses of the type w"1x"1+...+w"nx"n=@q were calculated for n=14161. The threshold functions at levels two and three were determined by computational experiments. The circuit was tested on various sets of 50+50 additional positive and negative examples. For depth-three circuits, we obtained a correct classification of about 97%. The input to the algorithm is derived from the DICOM standard representation of CT images. The simulated annealing procedure employs a logarithmic cooling schedule c(k)=@C/ln(k+2), where @C is a parameter that depends on the underlying configuration space. In our experiments, the parameter @C is chosen according to estimations of the maximum escape depth from local minima of the associated energy landscape.