Abstract: A method enables identification of a similarity level between a user-provided data item and a data item within a set of data documents. The method includes a representation generator determining, for each term in an enumeration of terms, occurrence information. The representation generator generates, for each term, a sparse distributed representation (SDR) using the occurrence information. The method includes receiving, by a filtering module, a filtering criterion. The method includes generating, by the representation generator, for the filtering criterion, at least one SDR. The method includes generating, by the representation generator, for a first of a plurality of streamed documents received from a data source, a compound SDR. The method includes determining, by a similarity engine executing on the second computing device, a distance between the filtering criterion SDR and the generated compound SDR. The method includes acting on the first streamed document, based upon the determined distance.

Abstract: A method enables identification of a similarity level between a user-provided data item and a data item within a set of data documents. The method includes a representation generator determining, for each term in an enumeration of terms, occurrence information. The representation generator generates, for each term, a sparse distributed representation (SDR) using the occurrence information. The method includes receiving, by a filtering module, a filtering criterion. The method includes generating, by the representation generator, for the filtering criterion, at least one SDR. The method includes generating, by the representation generator, for a first of a plurality of streamed documents received from a data source, a compound SDR. The method includes determining, by a similarity engine executing on the second computing device, a distance between the filtering criterion SDR and the generated compound SDR. The method includes acting on the first streamed document, based upon the determined distance.

Abstract: A method enables identification of a similarity level between a user-provided data item and a data item within a set of data documents. The method includes a representation generator determining, for each term in an enumeration of terms, occurrence information. The representation generator generates, for each term, a sparse distributed representation (SDR) using the occurrence information. The method includes receiving, by a filtering module, a filtering criterion. The method includes generating, by the representation generator, for the filtering criterion, at least one SDR. The method includes generating, by the representation generator, for a first of a plurality of streamed documents received from a data source, a compound SDR. The method includes determining, by a similarity engine executing on the second computing device, a distance between the filtering criterion SDR and the generated compound SDR. The method includes acting on the first streamed document, based upon the determined distance.

Abstract: A method of mapping data items to sparse distributed representations (SDRs) includes clustering in a two-dimensional metric space, by a reference map generator, a set of data documents selected according to at least one criterion, generating a semantic map. The semantic map associates a coordinate pair with each of the set of data documents. A parser generates an enumeration of data items occurring in the set of data documents. A representation generator determines, for each data item in the enumeration, occurrence information. The representation generator generates a distributed representation using the occurrence information. A sparsifying module receives an identification of a maximum level of sparsity. The sparsifying module reduces a total number of set bits within the distributed representation based on the maximum level of sparsity to generate an SDR having a normative fillgrade.

Abstract: A method enables identification of a similarity level between a user-provided data item and a data item within a set of data documents. The method includes a representation generator determining, for each term in an enumeration of terms, occurrence information. The representation generator generates, for each term, a sparse distributed representation (SDR) using the occurrence information. The method includes receiving, by a filtering module, a filtering criterion. The method includes generating, by the representation generator, for the filtering criterion, at least one SDR. The method includes generating, by the representation generator, for a first of a plurality of streamed documents received from a data source, a compound SDR. The method includes determining, by a similarity engine executing on the second computing device, a distance between the filtering criterion SDR and the generated compound SDR. The method includes acting on the first streamed document, based upon the determined distance.

Abstract: A method of mapping data items to sparse distributed representations (SDRs) includes clustering in a two-dimensional metric space, by a reference map generator, a set of data documents selected according to at least one criterion, generating a semantic map. The semantic map associates a coordinate pair with each of the set of data documents. A parser generates an enumeration of data items occurring in the set of data documents. A representation generator determines, for each data item in the enumeration, occurrence information. The representation generator generates a distributed representation using the occurrence information. A sparsifying module receives an identification of a maximum level of sparsity. The sparsifying module reduces a total number of set bits within the distributed representation based on the maximum level of sparsity to generate an SDR having a normative fillgrade.

Abstract: A computer-implemented method of training a neural network includes training a first neural network of a self organizing map type with a first set of first text documents each containing one or more keywords in a semantic context to map each document to a point in the self organizing map y semantic clustering; determining, for each keyword in the first set, all points in the self organizing map to which first documents containing said keyword are mapped, as a pattern and storing said pattern for said keyword in a pattern dictionary; forming at least one sequence of keywords from a second set of second text documents each containing one or more keywords in a semantic context; translating said at least one sequence of keywords into at least one sequence of patterns using the pattern dictionary; and training a second neural network with the at least one sequence of patterns.

Abstract: A computer-implemented method of training a neural network includes training a first neural network of a self organizing map type with a first set of first text documents each containing one or more keywords in a semantic context to map each document to a point in the self organizing map y semantic clustering; determining, for each keyword in the first set, all points in the self organizing map to which first documents containing said keyword are mapped, as a pattern and storing said pattern for said keyword in a pattern dictionary; forming at least one sequence of keywords from a second set of second text documents each containing one or more keywords in a semantic context; translating said at least one sequence of keywords into at least one sequence of patterns using the pattern dictionary; and training a second neural network with the at least one sequence of patterns.