Abstract: A method of training a neural net includes receiving a plurality of sets of data, each set representative of a plurality of inputs to the neural net and a resulting at least one output from the neural net and calculating a plurality of network weights for the neural network based on the received plurality of sets of data. Calculating the plurality of network weights including attributing greater weight in the calculation to at least one set of the plurality of sets of data than at least one other set of the plurality of sets of data.

Abstract: A computer-readable medium having computer-readable instructions stored thereon, which, when executed by a computer arrangement, configures the computer arrangement to perform a new plurality of steps. Those steps include retrieving data from a database. The data is associated with a plurality of assets, and the data for each asset includes information associated with the plurality features of the asset. The computer arrangement also groups the plurality of assets into at least one cluster based on the data and determines an associated hypothetical standard metric. A hypothetical standard metric comprises at least one of a Z-score corresponding to a deviation to a norm of the grouping and a curved line fitting. The computer arrangement also determines a degree of difference between each of a plurality of assets in the hypothetical standard metric and removes at least one asset of the plurality of assets to decrease the degree of difference.

Abstract: An apparatus and method for processing mixed data for a selected task is provided. An input transformation module converts mixed data into converted data. A functional mapping module processes the converted data to provide a functional output for the selected task. The selected task may be one or a combination of a variety of possible tasks, including search, recall, prediction, classification, etc. For example, the selected task may be for data mining, database search, targeted marketing, computer virus detection, etc.

Abstract: A method for automated feature selection is provided. One or more initial sets of features are generated and evaluated to determine quality scores for the feature sets. Selected ones of the feature sets are (i) chosen according to the quality scores and modified to generate a generation of modified feature sets, (ii) the modified feature sets are evaluated to determine quality scores for the modified feature sets, and (i) and (ii) are repeated until a modified feature set is satisfactory.

Abstract: A system and method for handling duration values includes associating a duration value with a start datetime to allow precise duration handling. Thus, a uniform time line approach may be used to handle duration arithmetic, comparisons, or other computations for duration values. For example, the uniform time line approach may characterize a duration value as a difference between two endpoints of a timeline segment having a length equal to the duration value. As a result, duration operations can be carried out with precision and accuracy using simple calendar calculations.

Abstract: A system for improving uniformity of assets within an entity includes a database for storing data. The data stored in the database is associated with a plurality of assets which are associated with a particular entity, and the data for each asset of the plurality of assets includes information associated with a plurality of features of the asset. For example, the plurality of features may include a vendor of the asset, an operating system of the asset, an age of the asset, a speed of the asset, an amount of memory associated with the asset, a number of computer processing units associated with the assets, and/or software installed on the asset.

Abstract: An apparatus and method for processing mixed data for a selected task is provided. An input transformation module converts mixed data into converted data. A functional mapping module processes the converted data to provide a functional output for the selected task. The selected task may be one or a combination of a variety of possible tasks, including search, recall, prediction, classification, etc. For example, the selected task may be for data mining, database search, targeted marketing, computer virus detection, etc.

Abstract: Pattern recognition of common modes by neural networks and other techniques are used to monitor and determine or predict the state of networks, computers, software systems, logical networks or other components of an information system, to report actual or predicted states, and to report other state characteristics.