Abstract: Embodiments of the present disclosure are directed to updating categorization of online content. An analytics engine implemented at least partially in hardware can receive an engagement indicator across a network interface; identify a type of the engagement indicator, the type of the engagement indicator comprising one of a positive engagement indicator or a negative engagement indicator; and update the reputation data stored in memory based on the type of the engagement indicator. A safe harbor time window is described during with user activity with online content is not reported to system administrators.

Abstract: In an example, there is disclosed a computing apparatus, including one or more logic elements, including at least one hardware logic element, comprising a classification engine to: receive a clean multi-labeled dataset comprising a plurality of document each assigned to one or more of a plurality of categories; receive an unclean multi-labeled dataset; and produce a recategorized and cleansed dataset from the unclean multi-labeled dataset, comprising predicting a number of labels {circumflex over (l)} for a document j, and comparing {circumflex over (l)} to an existing number of labels l. There is also disclosed a method of providing a classification engine.

Abstract: In an example, there is disclosed a computing apparatus, including one or more logic elements comprising a classification engine operable to identify at least one overlapping class pair comprising at least two classes selected from a class group, wherein the overlapping class pair may be characterized by a substantial mutual false positive rate; and assign an object to at least one assigned class selected from the class group.

Abstract: Embodiments of the present disclosure are directed to updating categorization of online content. An analytics engine implemented at least partially in hardware can receive an engagement indicator across a network interface; identify a type of the engagement indicator, the type of the engagement indicator comprising one of a positive engagement indicator or a negative engagement indicator; and update the reputation data stored in memory based on the type of the engagement indicator. A safe harbor time window is described during with user activity with online content is not reported to system administrators.

Abstract: Embodiments of the present disclosure are directed to updating categorization of online content. An analytics engine implemented at least partially in hardware can receive an engagement indicator across a network interface; identify a type of the engagement indicator, the type of the engagement indicator comprising one of a positive engagement indicator or a negative engagement indicator; and update the reputation data stored in memory based on the type of the engagement indicator. A safe harbor time window is described during with user activity with online content is not reported to system administrators.

Abstract: Particular embodiments described herein provide for an electronic device that can be configured to analyze data to determine one or more unique topics for a first class and one or more common topics that are common with the first class and a second class, where the topics that are common with the first class and the second class include one or more subtopics, assign one or more classifications to the data based, at least in part, on the one or more subtopics, and store the one or more classifications assigned to the data in memory. The one or more unique topics and one or more common topics can be determined by using a Jaccard Index. Also, the one or more subtopics can be determined using Latent Dirichlet Allocation.

Abstract: Particular embodiments described herein provide for an electronic device that can be configured to analyze data using an ensemble and assign a classification to the data based, at least in part, on the results of the analyses using the ensemble. The ensemble can include one or more multinomial classifiers and each multinomial classifier can assign two or more classifications to the data.

Abstract: In an example, there is disclosed a computing apparatus, including one or more logic elements, including at least one hardware logic element, comprising a classification engine to: receive a clean multi-labeled dataset comprising a plurality of document each assigned to one or more of a plurality of categories; receive an unclean multi-labeled dataset; and produce a recategorized and cleansed dataset from the unclean multi-labeled dataset, comprising predicting a number of labels {circumflex over (l)} for a document j, and comparing {circumflex over (l)} to an existing number of labels l. There is also disclosed a method of providing a classification engine.

Abstract: Embodiments of the present disclosure are directed to updating categorization of online content. An analytics engine implemented at least partially in hardware can receive an engagement indicator across a network interface; identify a type of the engagement indicator, the type of the engagement indicator comprising one of a positive engagement indicator or a negative engagement indicator; and update the reputation data stored in memory based on the type of the engagement indicator. A safe harbor time window is described during with user activity with online content is not reported to system administrators.

Abstract: In an example, there is disclosed a computing apparatus, including one or more logic elements comprising a classification engine operable to identify at least one overlapping class pair comprising at least two classes selected from a class group, wherein the overlapping class pair may be characterized by a substantial mutual false positive rate; and assign an object to at least one assigned class selected from the class group.