Abstract: A processing system including at least one processor may obtain a data set comprising a plurality of records, each record associating at least one feature value of at least one feature with a value of a target variable. The processing system may next segregate the plurality of records into a plurality of subsets based upon a range of values of the at least one feature and calculate a plurality of sub-volumes for the plurality of subsets, each sub-volume comprising a sum of the values of the target variable from records in a respective subset. The processing system may then generate a significance metric that is based on a difference between a highest sub-volume and a lowest sub-volume of the plurality of sub-volumes and select the at least one feature to train a classification model associated with the target variable, based upon the significance metric.

Abstract: Aspects of the subject disclosure may include, for example, dividing a feature range of a feature into a plurality of subsets that span the feature range, calculating an average target variable value for each subset of the plurality of subsets, resulting in a plurality of average target variable values, and estimating a measure of feature significance with respect to a target variable by determining a difference between a maximum average target variable value and a minimum average target variable value in the plurality of average target variable values. Other embodiments are disclosed.

Abstract: Improving configuration of routing areas within an area of interest can include obtaining input data including a list of common language location identifiers within a defined area of interest and identification of a number of routing areas to be created within the area of interest; creating a bounding rectangle around the area of interest represented by the list of common language location identifiers; generating, within the bounding rectangle, a uniformly distributed sequence point; determining, from within the list of common language location identifiers, if the uniformly distributed sequence point is located in a particular common language location identifier of the list of common language location identifiers; if so, adding, to a list of kernel common language location identifiers the uniformly distributed sequence point; and if not, discarding the uniformly distributed sequence point.

Abstract: Facilitating achievement of equilibrium between supply and demand of resources is provided herein. Operations of a system include determining a first amount of resources that are to satisfy first requests in a first area, and a second amount of resources that are to satisfy second requests in a second area and comparing the first amount of resources with a first quantity of available resources in the first area, and the second amount of resources with a second quantity of available resources in the second area. Further, based on the first quantity of available resources being less than the first amount of resources and the second quantity of available resources being more than the second amount of resources: classifying the first area as an overloaded condition, classifying the second area as an underloaded condition, and moving a resource of the second quantity of available resources to the first area.

Abstract: Facilitating selection of the most significant set of categorical features in machine learning is provided herein. Operations of a system include determining a list of unique values of a categorical variable. The operations also include calculating respective mean values, of a target variable, for unique values of the list of unique values of the categorical variable. Further, the operations include sorting the list of unique values by the respective mean values, resulting in a sorted list. The operations also include calculating respective derivatives of the respective mean values in the sorted list considering the respective mean values as a function and a number of the respective mean values in the sorted list as an independent variable. Additionally, the operations include determining a minimum derivative value over the sorted list and outputting the minimum derivative value as a resulting variable significance value.

Abstract: Various embodiments of the invention provide systems and methods for extracting information from digital documents, including physical documents that have been converted to digital documents. For example, some embodiments are configured to extract information from a field in a digital document by identifying a block of tokens before (i.e., a prior block) and a block of tokens after (i.e., a post block) the field from which the information is to be extracted, where both the prior block and post block are known to be associated with the field type of the field (e.g., name, address, phone number, etc.).

Abstract: Various embodiments of the invention provide systems and methods for extracting information from digital documents, including physical documents that have been converted to digital documents. For example, some embodiments are configured to extract information from a field in a digital document by identifying a block of tokens before (i.e., a prior block) and a block of tokens after (i.e., a post block) the field from which the information is to be extracted, where both the prior block and post block are known to be associated with the field type of the field (e.g., name, address, phone number, etc.).

Abstract: Concurrent Gradients Analysis (CGA), and two multi-objective optimization methods based on CGA are provided: Concurrent Gradients Method (CGM), and Pareto Navigator Method (PNM). Dimensionally Independent Response Surface Method (DIRSM) for improving computational efficiency of optimization algorithms is also disclosed. CGM and PNM are based on CGA's ability to analyze gradients and determine the Area of Simultaneous Criteria Improvement (ASCI). CGM starts from a given initial point, and approaches the Pareto frontier sequentially stepping into the ASCI area until a Pareto optimal point is obtained. PNM starts from a Pareto-optimal point, and steps along the Pareto surface in the direction that allows improving a subset of objective functions with higher priority. DIRSM creates local approximations based on automatically recognizing the most significant design variables.

Abstract: The Exclusion of Regions Method (TERM) and Concurrent Gradients Method (CGM) for multi-objective optimization of objective functions considered in a multi-dimensional domain are provided. TERM decomposes the domain into a set of non-intersecting sub-regions, and applies a special criterion to each sub-region to determine if it does not contain Pareto-points. Non-prospective sub-regions are filtered out, while prospective ones are used for generating points-candidates, and their improvement by a recursive procedure until pre-assigned accuracy is achieved. CGM works as any gradient-based algorithm. But on each step CGM determines the area of simultaneous objective functions improvement and a direction for the next step in this area. The area is determined in a simple way based on gradients for each objective function calculated on current point.

Abstract: Concurrent Gradients Analysis (CGA), and two multi-objective optimization methods based on CGA are provided: Concurrent Gradients Method (CGM), and Pareto Navigator Method (PNM). Dimensionally Independent Response Surface Method (DIRSM) for improving computational efficiency of optimization algorithms is also disclosed. CGM and PNM are based on CGA's ability to analyze gradients and determine the Area of Simultaneous Criteria Improvement (ASCI). CGM starts from a given initial point, and approaches the Pareto frontier sequentially stepping into the ASCI area until a Pareto optimal point is obtained. PNM starts from a Pareto-optimal point, and steps along the Pareto surface in the direction that allows improving a subset of objective functions with higher priority. DIRSM creates local approximations based on automatically recognizing the most significant design variables.

Abstract: Printing over a network by inputting print data to be printed and associated credit card information at a host terminal, uploading a print job comprising the print data to be printed and the associated credit card information to a print data storage server, inputting credit card information at an input device that communicates with the print data storage server, transmitting print data stored in the print data storage server having associated credit card information that corresponds to the credit card information input at the input device, and printing the print data on a printing device. The uploaded print job may be marked as ready for printing such that the print data transmitted to the printing device is that which has been marked as ready for printing. In addition, a display of pending print jobs may be provided for a user to select a print job to print prior to the print data being transmitted to the input device.

Abstract: The Exclusion of Regions Method (TERM) and Concurrent Gradients Method (CGM) for multi-objective optimization of objective functions considered in a multi-dimensional domain are provided. TERM decomposes the domain into a set of non-intersecting sub-regions, and applies a special criterion to each sub-region to determine if it does not contain Pareto-points. Non-prospective sub-regions are filtered out, while prospective ones are used for generating points-candidates, and their improvement by a recursive procedure until pre-assigned accuracy is achieved. CGM works as any gradient-based algorithm. But on each step CGM determines the area of simultaneous objective functions improvement and a direction for the next step in this area. The area is determined in a simple way based on gradients for each objective function calculated on current point.

Abstract: An interactive method of visualization and graphical analysis of functions considered in multi-dimensional domain is provided. The inventive method substitutes continuum objects represented by one or more functions by discrete approximation represented by a set of multi-dimensional points, to analyze the dataset graphically.

Abstract: An interactive method of visualization and graphical analysis of functions considered in multi-dimensional domain is provided. The inventive method substitutes continuum objects represented by one or more functions by discrete approximation represented by a set of multi-dimensional points, to analyze the dataset graphically.