Abstract: A technique for machine learning, such as supervised artificial neural network learning includes receiving data and checking the dimensionality of the read data and reducing the dimensionality to enhance machine learning performance using Principal Component Analysis methodology. The technique further includes specifying the neural network architecture and initializing weights to establish a connection between read data including the reduced dimensionality and the predicted values. The technique also includes performing supervised machine learning using the specified neural network architecture, initialized weights, and the read data including the reduced dimensionality to predict values. Predicted values are then compared to a normalized system error threshold value and the initialized weights are revised based on the outcome of the comparison to generate a learnt neural network having a reduced error in weight space.

Abstract: Computer-implemented methods for allocating resources to items are provided. One or more assignment scores for each item/resource pair are determined by applying one or more application-specific strategies to each item/resource pair using game theory. A cost matrix is created by first summing the assignment scores for each item/resource pair and then multiplying each assignment score sum by an assignment cost associated with assignment a particular resource to a particular item. Finally, an assignment solution is found by applying a Hungarian method to the cost matrix.

Abstract: An automated customer support tool. A web server receives one or more queries regarding the product from a customer. A categorizer coupled to the web server categorizes the received queries based on a type of query, to determine whether the answers to the queries can be automatically communicated to the customer. An FAQ extractor extracts one or more corresponding product FAQs from respective product FAQ data bases. A key-word extractor extracts one or more key-words from the received queries and the extracted product FAQs. The key-word extractor further transforms the extracted key words to unique numerical representations. An analyzer coupled to the key-word extractor represents the transformed key-words into respective query and product FAQ vector forms. The analyzer further applies a convolution algorithm to each of the query vector forms, with each of the product FAQ vector forms separately and obtains one or more appropriate answers to the queries.

Abstract: Data structures, systems, and methods are aspects of pattern recognition using observable operator models (OOMs). OOMs are more efficient than Hidden Markov Models (HMMs). A data structure for an OOM has characteristic events, an initial distribution vector, a probability transition matrix, an occurrence count matrix, and at least one observable operator. System applications include computer systems, cellular phones, wearable computers, home control systems, fire safety or security systems, PDAs, and flight systems. A method of pattern recognition comprises training OOMs, receiving unknown input, computing matching probabilities, selecting the maximum probability, and displaying the match. A method of speech recognition comprises sampling a first input stream, performing a spectral analysis, clustering, training OOMs, and recognizing speech using the OOMs.

Abstract: A system and a method for an automated intelligent information mining includes receiving product-related queries and respective product-related information from various text sources; extracting multiple key-phrases from the product-related information and received queries; generating two or more layers of contextual relation maps by mapping the extracted key-phrases to two-dimensional maps using a self organizing map, and a technique including a combination of Hessian matrix and Perturbation technique to enhance the learning process and to categorize the extracted key-phrases based on a contextual meaning. Further, the technique includes forming word clusters and constructing corresponding key phrase frequency histograms for each of the generated contextual relation maps.

Abstract: Data structures, systems, and methods are aspects of pattern recognition using observable operator models (OOMs). OOMs are more efficient than Hidden Markov Models (HMMs). A data structure for an OOM has characteristic events, an initial distribution vector, a probability transition matrix, an occurrence count matrix, and at least one observable operator. System applications include computer systems, cellular phones, wearable computers, home control systems, fire safety or security systems, PDAs, and flight systems. A method of pattern recognition comprises training OOMs, receiving unknown input, computing matching probabilities, selecting the maximum probability, and displaying the match. A method of speech recognition comprises sampling a first input stream, performing a spectral analysis, clustering, training OOMs, and recognizing speech using the OOMs.

Abstract: A technique for machine learning, such as supervised artificial neural network learning includes receiving data and checking the dimensionality of the read data and reducing the dimensionality to enhance machine learning performance using Principal Component Analysis methodology. The technique further includes specifying the neural network architecture and initializing weights to establish a connection between read data including the reduced dimensionality and the predicted values. The technique also includes performing supervised machine learning using the specified neural network architecture, initialized weights, and the read data including the reduced dimensionality to predict values. Predicted values are then compared to a normalized system error threshold value and the initialized weights are revised based on the outcome of the comparison to generate a learnt neural network having a reduced error in weight space.

Abstract: Systems and methods are provided through which the quantity of one or more items in a customer order are reduced in reference to a function of inverse probability of vendor profit and in reference to a reasonable margin of a target time predetermined by the customer, when the customer order cannot be produced within the margin. The reduced item quantities update the corresponding items in the customer order, and the items in the customer order are produced accordingly, such that the objective of the business goal is met or not sacrificed.

Abstract: In a first embodiment, the present invention provides computer-implemented methods for allocating resources to items are provided. Methods according to the present invention first determine one or more assignment scores for each item/resource pair by applying one or more application-specific strategies to each item/resource pair using game theory. A cost matrix is created by first summing the assignment scores for each item/resource pair and then multiplying each assignment score sum by an assignment cost associated with assignment a particular resource to a particular item. Finally, an assignment solution is found by applying the Hungarian method to the cost matrix. In a second embodiment, the present invention provides a computer-readable medium having computer-executable instructions for performing a methods of the present invention.