1. A computer-implemented tagging system, comprising: a learning component for learning user tagging behavior related to tagged data by analyzing an application of tags to messages by a user, wherein the learning component generates tokens from the tagged data, maps the tokens to a tag the user has applied and employs a conjugate gradient regression algorithm to learn the user tagging behavior by separating the tagged data according to an n-dimensional categorization process; a selection component for automatically predicting and selecting one or more tags from a tagset based on prior tagging habits of the user, as indicated by the learned user tagging behavior; a tagging component for automatically tagging new data entering the system by automatically applying the one or more tags based on the prior tagging habits, indicated by the learned user tagging behavior, to enable auto-tagging for reducing a tagging burden of the user, wherein the automatically applied tag may be overridden by the user; a feedback path for feeding back at least a portion of the tagged new data into the tagged data, at least a portion of which was previously used as training data, to perform retraining of the learning component to adapt to new user tagging behavior, wherein the retraining is performed periodically or in response to a predetermined threshold number of automatically applied tags being overridden by the user; and a processor that executes computer-executable instructions associated with at least one of the learning component, the selection component, or the tagging component.
1. A computer-implemented tagging system, comprising: a learning component for learning user tagging behavior related to tagged data by analyzing an application of tags to messages by a user, wherein the learning component generates tokens from the tagged data, maps the tokens to a tag the user has applied and employs a conjugate gradient regression algorithm to learn the user tagging behavior by separating the tagged data according to an n-dimensional categorization process; a selection component for automatically predicting and selecting one or more tags from a tagset based on prior tagging habits of the user, as indicated by the learned user tagging behavior; a tagging component for automatically tagging new data entering the system by automatically applying the one or more tags based on the prior tagging habits, indicated by the learned user tagging behavior, to enable auto-tagging for reducing a tagging burden of the user, wherein the automatically applied tag may be overridden by the user; a feedback path for feeding back at least a portion of the tagged new data into the tagged data, at least a portion of which was previously used as training data, to perform retraining of the learning component to adapt to new user tagging behavior, wherein the retraining is performed periodically or in response to a predetermined threshold number of automatically applied tags being overridden by the user; and a processor that executes computer-executable instructions associated with at least one of the learning component, the selection component, or the tagging component. 2. The system of claim 1 , wherein the data is an incoming message.