Abstract: A method for generating optimized tinnitus masks includes, for example, presenting stimuli in succession to a user, receiving the user's rating of the effectiveness of each presented stimulus in reducing tinnitus, selecting a subset of the stimuli to maintain as tinnitus masks, and, generating variant stimuli from the subset of the stimuli that is maintained as tinnitus masks.

Abstract: One aspect of the invention is a method for identifying at least one property of data. An example of the method includes receiving data, and making assessments regarding the data. The method also includes applying at least one behavioral operator, and outputting results. The method further comprises receiving feedback concerning system performance. Additionally, the method includes adjusting at least one parameter based on the feedback received concerning system performance, wherein the at least one parameter is a parameter of a machine learning method.

Abstract: One aspect of the invention is a method for assigning categorical data to a plurality of clusters. The method may include identifying a plurality of categories associated with the data. The method also may include, for each category in the plurality of categories, identifying at least one element associated with the category. The method also may include specifying a number of clusters to which the data may be assigned. The method additionally may include assigning at least some of the data, wherein each assigned datum is assigned to a respective one of the clusters. The method further may include, for at least one of the clusters, determining, for at least one category, the frequency in data assigned to the cluster of at least one element associated with the category. Further, the invention may provide for detecting outliers, anomalies, and exemplars in the categorical data.

Abstract: One aspect of the invention is a method for identifying at least one property of data. An example of the method includes receiving data, and making assessments regarding the data. The method also includes applying at least one behavioral operator, and outputting results. The method further comprises receiving feedback concerning system performance. Additionally, the method includes adjusting at least one parameter based on the feedback received concerning system performance, wherein the at least one parameter is a parameter of a machine learning method.

Abstract: One aspect of the invention is a method for assigning categorical data to a plurality of clusters. The method includes identifying a plurality of categories associated with the data. The method also includes, for each category in the plurality of categories, identifying at least one element associated with the category. The method also includes specifying a number of clusters to which the data may be assigned. The method additionally includes assigning at least some of the data, wherein each assigned datum is assigned to a respective one of the clusters. The method further includes, for at least one of the clusters, determining, for at least one category, the frequency in data assigned to the cluster of at least one element associated with the category. Further, the invention provides for detecting outliers, anomalies, and exemplars in the categorical data.

Abstract: One aspect of the invention is a method for identifying at least one property of data. An example of the method includes receiving data, and making assessments regarding the data. The method also includes applying at least one behavioral operator, and outputting results. The method further comprises receiving feedback concerning system performance. Additionally, the method includes adjusting at least one parameter based on the feedback received concerning system performance, wherein the at least one parameter is a parameter of a machine learning method.