Abstract: Systems and methods of a knowledge management networking are disclosed here. In one aspect, embodiments of the present disclosure include a method, which may be implemented on a system, of hosting a web-space having a plurality of objects, the plurality of objects to include one or more of, representations of a set of users, a set of web-items, and a set of nets; wherein a net of the set of nets is a subset of the web-space comprising a sub-plurality of the plurality of objects.

Abstract: Representative embodiments are directed to systems and methods for processing training data for a statistical classification application. In one embodiment, confidence values are calculated for training data elements to identify the probabilities of the training data elements belonging to identified classes. An interactive scatter plot is generated using the calculated confidence values. The scatter plot visually indicates the confidence values of points in the scatter plot. Accordingly, the user is able to identify potentially misclassified training data elements. The user may select training data elements from the scatter plot and reclassify training elements as appropriate. Upon reclassification, the confidence values may be recalculated and the scatter plot revised.

Abstract: Systems and methods of a knowledge management networking are disclosed here. In one aspect, embodiments of the present disclosure include a method, which may be implemented on a system, of hosting a web-space having a plurality of objects, the plurality of objects to include one or more of, representations of a set of users, a set of web-items, and a set of nets; wherein a net of the set of nets is a subset of the web-space comprising a sub-plurality of the plurality of objects.

Abstract: Embodiments of the present invention use the results of a plurality of defect detecting tests and adjust the result of each test in a manner that reflects the accuracy of the test that produced it. A manufactured unit can then be evaluated using a mathematical combination of the adjusted results.

Abstract: Hierarchical classification of samples. First stage classification identifies most members of the majority class and removes them from further consideration. Second stage classification then focuses on discriminating between the minority class and the greatly reduced number of majority class samples lying near the decision boundary.