Abstract: A system and a method determine a value for a parameter. Reference values for the parameter are determined from a group of objects. A first technique is used by the system for determining for each object the reference value from a first set of data. A learning dataset is created by associating for each object of the group of objects a second set of data and the reference value. The second set of data is acquired by the system according to a second technique for determining values of the parameter and is configured for enabling a determination of the parameter. A machine learning technique trained on the learning dataset is used for determining a value of the parameter. The second set of data obtained for each of the objects is used as input in a machine learning algorithm and its associated reference value is used as output target.

Abstract: A technique for performing a financial calculation is described. In this calculation technique, initial financial values are rounded based on a rounding criterion, and a total financial value is calculated by summing the rounded financial values. Based on the rounded financial values, associated rounding error values are computed. These rounding error values are then summed to determine a total error value. Moreover, the total error value is rounded based on the rounding criterion, and the resulting rounded total error value is used to correct a rounding error in the total financial value.

Abstract: Methods for multi-class cost-sensitive learning are based on iterative example weighting schemes and solve multi-class cost-sensitive learning problems using a binary classification algorithm. One of the methods works by iteratively applying weighted sampling from an expanded data set, which is obtained by enhancing each example in the original data set with as many data points as there are possible labels for any single instance, using a weighting scheme which gives each labeled example the weight specified as the difference between the average cost on that instance by the averaged hypotheses from the iterations so far and the misclassification cost associated with the label in the labeled example in question. It then calls the component classification algorithm on a modified binary classification problem in which each example is itself already a labeled pair, and its (meta) label is 1 or 0 depending on whether the example weight in the above weighting scheme is positive or negative, respectively.

Abstract: A scheduling system and method for moving plural objects through a multipath system described as a freight railway scheduling system. The scheduling system utilizes a cost reactive resource scheduler to minimize resource exception while at the same time minimizing the global costs associated with the solution. The achievable movement plan can be used to assist in the control of, or to automatically control, the movement of trains through the system.

Abstract: The invention concerns heuristic algorithms for the classification of Objects. A first learning algorithm comprises a genetic algorithm that is used to abstract a data stream associated with each Object and a pattern recognition algorithm that is used to classify the Objects and measure the fitness of the chromosomes of the genetic algorithm. The learning algorithm is applied to a training data set. The learning algorithm generates a classifying algorithm, which is used to classify or categorize unknown Objects. The invention is useful in the areas of classifying texts and medical samples, predicting the behavior of one financial market based on price changes in others and in monitoring the state of complex process facilities to detect impending failures.

Abstract: A regulation method for decision tree construction is described wherein decision rules can be automatically adjusted between crisp and soft decisions. Starting with a conventional decision tree, additional statistics are stored at the terminal and non-terminal nodes during training and used during application to new samples. The regulation process allows automatic determination of tree structure. It also allows incremental updating of a regulation decision tree with graceful change to classification performance characteristics. Compound regulation decision trees are described for use to update the decision structure when new training input samples include new classes. Methods for pruning regulation decision trees, for focusing regulation decision trees, for determining optimal depth and regulation parameters and for determining optimal sample weighting are taught.

Abstract: The invention provides a method and apparatus for combining two or more risk models to create a risk model with wider scope than its constituent parts. The method insures that the newly formed risk model is consistent with the component models from which it is formed.

Abstract: The order of a group of objects is reversed in logarithmic time. The invention may be applied to a wide variety of applications. An exemplary embodiment is included in which the invention may be used to reverse the order of the bits contained in a computer register.

Abstract: A resource scheduler for scheduling railway train resources over a track system with a high degree of optimization. The scheduler is implemented in an expert system that employs simulated annealing techniques to approximate the optimum solution.