Abstract: The present invention is a new method directed for detecting anomalies in monitored data having plurality of data-segments partitioned to context related initial-subspaces, the method comprising: training an association-map between the initial-subspaces and feature-clusters of the plurality of data-segments, the training is responsive to a fit-criterion; concatenating the initial-subspaces into cluster-subspaces, responsive to being associated to similar feature-clusters according to the association-map, to obtain a generalized-association-map; pinpointing at least one anomaly of at least one new data-segment of the data, responsive to deviation-criterion for deviation of the new data-segment from its association to one of the feature-clusters, according to the generalized-association-map; and triggering an automatic-act responsive to a trigger-criterion for the at least one anomaly.

Abstract: Method and system for classification in imbalanced datasets within a supervised classification framework. Bootstrap methodology is modified according to sampling weights and adaptive target set size principle, to induce weak classifiers from the bootstrap samples in an iterative procedure that results in a set of weak classifiers. A weighted combination scheme is used to adaptively combine the weak classifiers to a strong classifier that achieves good performance for all classes (reflected as high values for metrics such as G-mean and F-score) as well as good overall accuracy.

Abstract: An object-recognition method and system employing Bayesian fusion algorithm to reiteratively improve probability of correspondence between captured object images and database object images by fusing probability data associated with each of plurality of object image captures.

Abstract: Method and system for classification in imbalanced datasets within a supervised classification framework. Bootstrap methodology is modified according to k-Nearest Neighbor sampling weights and adaptive target set size principle, to induce weak classifiers from the bootstrap samples in an iterative procedure that results in a set of weak classifiers. A weighted combination scheme is used to adaptively combine the weak classifiers to a strong classifier that achieves good performance for all classes (reflected as high values for metrics such as G-mean and F-score) as well as good overall accuracy.

Abstract: The present invention is a new method directed for detecting anomalies in monitored data having plurality of data-segments partitioned to context related initial-subspaces, the method comprising: training an association-map between the initial-subspaces and feature-clusters of the plurality of data-segments, the training is responsive to a fit-criterion; concatenating the initial-subspaces into cluster-subspaces, responsive to being associated to similar feature-clusters according to the association-map, to obtain a generalized-association-map; pinpointing at least one anomaly of at least one new data-segment of the data, responsive to deviation-criterion for deviation of the new data-segment from its association to one of the feature-clusters, according to the generalized-association-map; and triggering an automatic-act responsive to a trigger-criterion for the at least one anomaly.

Abstract: An object-recognition method and system employing Bayesian fusion algorithm to reiteratively improve probability of correspondence between captured object images and database object images by fusing probability data associated with each of plurality of object image captures.