Abstract: Techniques for a geofence generation feature are described herein. Geospatial vector data for a geographical area and seed points for delivery locations within the geographical area may be obtained. A plurality of polygons that represent the geographical area may be determined based on an algorithm that uses the seed points and the geospatial vector data. A boundary point for each polygon may be determined based on coordinates for the plurality of polygons where each boundary point corresponds to a single seed point. A geofence of a first size may be generated, for each polygon, based on the coordinates for the polygons, the seed point, and the boundary point. Historical scans of GPS data may be obtained from user devices during historical visits to the geographical area. The geofence may be modified to a second size or a different shape based on the historical scans of the GPS data.

Abstract: Embodiments of the present invention are directed to a method for information management. In one embodiment, the method includes providing an organizational directory comprising hierarchical information regarding an organization and receiving relationship data including an association of a first person to a second person. The method also includes querying the organizational directory and the relationship data to identify a third person, wherein the third person is a member of the organization and identifying the third person comprises determining a path including the third person in an association network.

Abstract: Provided are systems, methods and techniques for machine-learning classification. In one representative embodiment, an item having values for a plurality of different features in a feature set is obtained, together with scores for the different features. The score for a given feature is a measure of prediction ability for that feature and was calculated as a function of a plurality of different occurrence metrics of the feature. The values for the features are scaled according to the scores for the features, and the item is classified by inputting the adjusted feature set values for the item into a previously trained classifier.

Abstract: One or more machine learning classifiers are trained to classify cases in one or more categories using one or more sets of labeled training data. A first distribution of scores for positive cases in the training set is determined for each category, and a second distribution of scores for negative cases in the training set is determined for each category. A third distribution of scores is generated by each classifier classifying cases in a set of target data is also determined. A proportion of cases in the target set that are positive cases for a category is estimated by fitting the first distribution and the second distribution for the category to the third distribution.

Abstract: A related feature determination system determines a feature related to an indication of a concept. A set of positive cases and a set of negative case are selected using the indication of a concept. A classifier is induced for the concept using the set of positive cases in a manner blinded to the indication of a concept. The set of negative cases is applied to the classifier. A feature related to the indication of a concept is determined using results of applying the classifier to the set of negative cases.

Abstract: A method of reporting the presentation of data, a data storage medium, and a system for employing a web page is disclosed and that includes accessing a source of data having a specific segment, and reporting a presentation of the specific segment to a remote location.

Abstract: Provided are, inter alia, systems, methods and techniques for facilitating identification of a skilled resource's skill set. Initially, a master set of previously identified skills is filtered in order to identify a subset of skills that appear to be potentially applicable to a skilled resource. Then, based on the identified subset, a query regarding skills actually possessed by the skilled resource is created and presented. A response that designates skills possessed by the skilled resource is received and incorporated into a user skills database. The above-referenced filtering is performed by utilizing an automated tool to analyze content from a set of computer files.

Abstract: Provided are systems, methods and techniques for hierarchically segmenting a sequence of records, by specifying windows, each window encompassing a portion of the sequence. For each specified window, a function of the data records within that specified window is calculated in order to generate a function value for the window. The function values are then used to define a hierarchical arrangement in which the sequence is divided into a plurality of segments and the segments are divided into smaller segments.

Abstract: A feature selection method for selecting a predetermined number of features, the method may comprise for each category, ranking features according to feature ranking for two-category problems; and while the predetermined number of features has not yet been selected: picking a category c; selecting a feature based on the ranking for category c; and adding the selecting feature to an output list if not already present in the output list and removing the selected feature from further consideration for the category c. An apparatus and computer program code are also provided.

Abstract: A method for predicting parts for onsite repair which takes into account a repair history and the costs associated with mis-predictions. Parts for onsite repair of a product are predicted by determining an expected waste for one or more parts of the product. The parts having a lowest expected waste are selected and sent to the onsite repair. The expected waste indicates parts that are responsible for high support costs and highlights the mistakes being made and scores the mistakes by actual cost.

Abstract: Provided are systems, methods and techniques for classifying items. According to one preferred embodiment, initial feature sets are obtained for a current batch of items, and classification predictions are generated for the items based on their initial feature sets, using a set of existing classifiers. The classification predictions are then appended as additional features to the respective feature sets of the items, thereby obtaining enhanced feature sets, and a first classifier is trained, using a plurality of the items as training samples and using the enhanced feature sets of the training samples. Finally, items in the current batch are classified using their enhanced feature sets and the first classifier. According to this embodiment, the existing classifiers were trained on a plurality of different sets of items that are representative of corresponding different times.

Abstract: A count of cases in a target set classified in a class is computed using a classifier. In the method, one or more measures of behavior of the classifier are determined for a plurality of classification thresholds and at least one classification threshold for the classifier is selected based on the one or more measures of behavior. In addition, a score for a plurality of the cases in the target set using the classifier is computed and the count of the cases is computed based on the scores, the selected at least one classification threshold, and the one or more measures of behavior.

Abstract: A two-class analysis system for summarizing features and determining features appropriate to use in training a classifier related to a data mining operation. Exemplary embodiments describe how to select features which will be suited to training a classifier used for a two-class text classification problem. Bi-Normal Separation methods are defined wherein there is a measure of inverse cumulative distribution function of a standard probability distribution and representative of a difference between occurrences of the feature between said each class. In addition to training a classifier, the system provides a means of summarizing differences between classes.

Abstract: A first set of classifiers are trained to predict categories using attributes of cases. A second set of a classifiers are also trained to predict the categories using the attributes of the cases. In addition, the second set of classifiers use the predictions from at least one of the classifiers in the first set to make predictions.

Abstract: Provided are, inter alia, systems, methods and techniques for facilitating identification of a skilled resource's skill set. Initially, a master set of previously identified skills is filtered in order to identify a subset of skills that appear to be potentially applicable to a skilled resource. Then, based on the identified subset, a query regarding skills actually possessed by the skilled resource is created and presented. A response that designates skills possessed by the skilled resource is received and incorporated into a user skills database. The above-referenced filtering is performed by utilizing an automated tool to analyze content from a set of computer files.

Abstract: Provided are systems, methods and techniques for machine-learning classification. In one representative embodiment, an item having values for a plurality of different features in a feature set is obtained, together with scores for the different features. The score for a given feature is a measure of prediction ability for that feature and was calculated as a function of a plurality of different occurrence metrics of the feature. The values for the features are scaled according to the scores for the features, and the item is classified by inputting the adjusted feature set values for the item into a previously trained classifier.

Abstract: The present invention relates generally to the classification of items into categories, and more generally, to the automatic selection of different classifiers at different places within a hierarchy of categories. An exemplary hierarchical categorization method uses a hybrid of different classification technologies, with training-data based machine-learning classifiers preferably being used in those portions of the hierarchy above a dynamically defined boundary in which adequate training data is available, and with a-priori classification rules not requiring any such training-data being used below that boundary, thereby providing a novel hybrid categorization technology that is capable of leveraging the strengths of its components.

Abstract: A system for detecting at least one environmental condition in a room. The system includes one or more detecting devices having a sensor configured to detect the at least one environmental condition and to display the detected at least one environmental condition. The system also includes one or more reader devices configured to image the one or more detecting devices and a controller configured to communicate with the one or more reader devices. The controller is operable to control the one or more reader devices and to receive the images from the one or more reader devices. In addition, the controller is configured to determine the at least one environmental condition based upon the images of the one or more detecting devices. The system further includes a memory accessible by the controller, and the controller is configured to store the at least one environmental condition in the memory.

Abstract: Generating masks for de-duplication in a database where distributed entities provide activity data for said database. Determining from activity input data which entities add variable data to a given data field. Generating a list of the masks which effectively remove the variable data portion in the field. Consolidating input data using the generated masks.

Abstract: A method and system is provided for categorization of an item. A plurality of categories is organized in a hierarchy of categories with a plurality of categorizers corresponding to the plurality of categories. A featurizer featurizes the item to create a list of item features. The list is used in a categorizer system, including the plurality of categorizers, for determining a plurality of levels of goodness. The item is categorized in the categorizer system in the plurality of categories based on the respective plurality of levels of goodness. The best categories are processed first, allowing termination at any time. The system successively refines an item down a hierarchy, until the features no longer support further refinement. The categories and the degree of support for the categories are provided.