Abstract: Methods and apparatuses are provided for accessing taxonomic data associated with an item as classified into a taxonomy having a hierarchical structure, establishing dependency data associated with a distribution represented in the taxonomic data, and determining entropic data for the item based, at least in part, on the distribution and established dependency.

Abstract: The disclosed technology uses a visibility function within a network environment that includes a set of nodes. At least one of nodes can directly transmit to one or more of a subset of the set of nodes. The visibility function characterizes a non-uniform resolution profile within the network environment that extends over at least one node outside the subset of nodes. The sent situation information is conditioned to propagate through the network environment according to the visibility function. The node can also receive situation information that includes a visibility parameter. Once the situation information is received, the node can evaluate the visibility parameter to determine whether the situation information is eligible for continued propagation through the network environment. If situation information is eligible for continued propagation the node then prepares to transmit the situation information.

Abstract: The disclosed technology uses a visibility function within a network environment that includes a set of nodes. At least one of nodes can directly transmit to one or more of a subset of the set of nodes. The visibility function characterizes a non-uniform resolution profile within the network environment that extends over at least one node outside the subset of nodes. The sent situation information is conditioned to propagate through the network environment according to the visibility function. The node can also receive situation information that includes a visibility parameter. Once the situation information is received, the node can evaluate the visibility parameter to determine whether the situation information is eligible for continued propagation through the network environment. If situation information is eligible for continued propagation the node then prepares to transmit the situation information.

Abstract: A method of classifying samples to one of a number of predetermined classes involves using a number of class models or classifiers to form order statistic for each classifier. A linear combination of the order statistic (L-statistic) is calculated to determine the confidence of that particular classifier, both in general and for that particular sample. Relative weights are then derived from these confidences, and used to calculate a weighted summation across all classifiers for each class of the likelihoods that a sample belongs to that class. The sample is classified in the class which has the associated weighted summation which is greatest in value.

Abstract: A method of classifying samples to one of a number of predetermined classes involves using a number of class models or classifiers to form order statistic for each classifier. A linear combination of the order statistic (L-statistic) is calculated to determine the confidence of that particular classifier, both in general and for that particular sample. Relative weights are then derived from these confidences, and used to calculate a weighted summation across all classifiers for each class of the likelihoods that a sample belongs to that class. The sample is classified in the class which has the associated weighted summation which is greatest in value.