A method includes: partitioning learning data containing objective variables and explanatory variables into a plurality of subsets of data; executing regularization processing on first data in each of the partitioned subsets, and extracting a first element equal to zero; extracting, as a candidate, each model where an error ratio between first multiple regression and second multiple regression is equal to or more than a predetermined value, the first multiple regression being a result of multiple regression on second data which is test data in each of the partitioned subsets and is for use to calculate the error ratio of the learning data, the second multiple regression being a result of multiple regression on third data obtained by excluding the first element from the second data; and outputting a model where zero is substituted for an element that takes zero a predetermined or larger number of times in the candidate.

A method includes: partitioning learning data containing objective variables and explanatory variables into a plurality of subsets of data; executing regularization processing on first data in each of the partitioned subsets, and extracting a first element equal to zero; extracting, as a candidate, each model where an error ratio between first multiple regression and second multiple regression is equal to or more than a predetermined value, the first multiple regression being a result of multiple regression on second data which is test data in each of the partitioned subsets and is for use to calculate the error ratio of the learning data, the second multiple regression being a result of multiple regression on third data obtained by excluding the first element from the second data; and outputting a model where zero is substituted for an element that takes zero a predetermined or larger number of times in the candidate.

Enhanced techniques and circuitry are presented herein for providing responses to questions from among digital documentation sources spanning various documentation formats, versions, and types. One example includes a method comprising receiving an indication of a question directed to subject having a documentation corpus, determining a set of passages of the documentation corpus related to the question, ranking the set of passages according to relevance to the question, forming semantic clusters comprising sentences extracted from ranked ones of the set of passages according to sentence similarity, and providing a response to the question based at least on a selected semantic cluster.

Enhanced techniques and circuitry are presented herein for providing responses to questions from among digital documentation sources spanning various documentation formats, versions, and types. One example includes a method comprising receiving an indication of a question directed to subject having a documentation corpus, determining a set of passages of the documentation corpus related to the question, ranking the set of passages according to relevance to the question, forming semantic clusters comprising sentences extracted from ranked ones of the set of passages according to sentence similarity, and providing a response to the question based at least on a selected semantic cluster.

Techniques for improved neural network modeling are provided. In one embodiment, a system comprises a memory that stores computer-executable components and a processor that executes the components. The computer-executable components can comprise a loss function logic component that determines a penalty based on a training term, the training term being a function of a relationship between an output scalar value of a first neuron of a plurality of neurons of a neural network model, a plurality of input values from the first neuron, and one or more tunable weights of connections between the plurality of neurons; an optimizer component that receives the penalty from the loss function component, and changes one or more of the tunable weights based on the penalty; and an output component that generates one or more output values indicating whether a defined pattern is detected in unprocessed input values received at the neural network evaluation component.

Techniques for improved neural network modeling are provided. In one embodiment, a system comprises a memory that stores computer-executable components and a processor that executes the components. The computer-executable components can comprise a loss function logic component that determines a penalty based on a training term, the training term being a function of a relationship between an output scalar value of a first neuron of a plurality of neurons of a neural network model, a plurality of input values from the first neuron, and one or more tunable weights of connections between the plurality of neurons; an optimizer component that receives the penalty from the loss function component, and changes one or more of the tunable weights based on the penalty; and an output component that generates one or more output values indicating whether a defined pattern is detected in unprocessed input values received at the neural network evaluation component.

Provided is an apparatus for deriving a social relation between accounts based on a transaction ledger, which includes: a data storage unit storing account information, software for deriving an inter-account social relation, and inter-account social relation information; and a processor deriving the social relation between the accounts based on a transaction ledger generated by transactions among users with the accounts.

Provided is an apparatus for deriving a social relation between accounts based on a transaction ledger, which includes: a data storage unit storing account information, software for deriving an inter-account social relation, and inter-account social relation information; and a processor deriving the social relation between the accounts based on a transaction ledger generated by transactions among users with the accounts.

A method for visualizing and analyzing contributions of various input features for traffic safety status prediction is provided. The method includes initializing a deep belief network (DBN) with input features; performing unsupervised learning/training by observing changes of weights of the input features during the unsupervised learning/training; when the unsupervised learning/training process is complete, performing supervised learning/training process by generating a reconstructed input layer based on results of each hidden layer; and continually running the supervised learning/training and generating a weight diagram based on both visualization and numerical analysis that calculates contributions of the input features. The input features may include one or more of annual average daily commercial traffic (AADCT), median width, left shoulder width, right shoulder width, curve deflection, and exposure for traffic safety status prediction.

A method for visualizing and analyzing contributions of various input features for traffic safety status prediction is provided. The method includes initializing a deep belief network (DBN) with input features; performing unsupervised learning/training by observing changes of weights of the input features during the unsupervised learning/training; when the unsupervised learning/training process is complete, performing supervised learning/training process by generating a reconstructed input layer based on results of each hidden layer; and continually running the supervised learning/training and generating a weight diagram based on both visualization and numerical analysis that calculates contributions of the input features. The input features may include one or more of annual average daily commercial traffic (AADCT), median width, left shoulder width, right shoulder width, curve deflection, and exposure for traffic safety status prediction.