Abstract: A computer-readable recording medium storing a program for causing a computer to execute processing including: acquiring a first determination result of first graph data by performing determination processing on the first graph data; acquiring one or more first scores regarding a feature of the first graph data by using a trained model, the one or more first scores representing a basis of the first determination result of the first graph data, the trained model being a model configured to output, in response to obtaining graph data, one or more scores regarding the feature of the graph data; in a case where all of the one or more first scores are less than a threshold, specifying second graph data being a second determination result different from the first determination result; and outputting, in association with the first determination result, information regarding the feature of the second graph data.

Abstract: A non-transitory computer-readable recording medium storing a model generation program for causing a computer to perform processing including: changing first data and generating a plurality of pieces of data; calculating a plurality of values indicating a distance between the first data and each of the plurality of pieces of data; determining whether or not a value indicating uniformity of distribution of the distance between the first data and each of the plurality of pieces of data is equal to or greater than a threshold based on the plurality of values; and in a case where the value indicating the uniformity is determined to be equal to or greater than the threshold, generating a linear regression model using a result obtained by inputting the plurality of pieces of data into a machine learning model as an objective variable and using the plurality of pieces of data as explanatory variables.

Abstract: A recording medium storing an explanatory program for causing a computer to execute an explanatory process. The process includes: generating a plurality of pieces of data based on first data; calculating a ratio of output results, among a plurality of results output in a case that each of the plurality of pieces of data is input to a machine learning model, different from first results output in a case that the first data is input to the machine learning model; generating a linear model based on the plurality of pieces of data and the plurality of results in a case that the calculated ratio satisfies a criterion; and outputting explanatory information with respect to the first results based on the linear model.

Abstract: An apparatus identifies partial tensor data that contributes to machine learning using tensor data in a tensor format obtained by transforming training data having a graph structure. Based on the partial tensor data and the training data, the apparatus generates expanded training data to be used in the machine learning by expanding the training data.

Abstract: A machine learning apparatus calculates second values, based on first values each assigned to one variable value of each term in relation to a neuron in a layer following an input layer of a neural network, where the second values are assigned to variable-value combination patterns in relation to each following-layer neuron. Each second value is represented by a product of first values each assigned to a variable value included in the combination pattern in relation to the following-layer neuron. The apparatus then applies the second values as weights each to a numerical value when it is entered to the corresponding following-layer neuron, to calculate an output value of the neural network with the numerical values arranged in an input order. The apparatus updates reference values in a reference pattern and the first values based on input error that the output value exhibits with respect to training data.

Abstract: A learning method implemented by a computer, includes: creating an input data tensor including a local dimension and a universal dimension by partitioning series data into local units, the series data including a plurality of elements, each of the plurality of elements in the series data being logically arranged in a predetermined order; and performing machine learning by using tensor transformation in which a transformation data tensor obtained by transforming the input data tensor with a transformation matrix is outputted using a neural network, wherein the learning includes rearranging the transformation matrix so as to maximize a similarity to a matching pattern serving as a reference in the tensor transformation regarding the universal dimension of the input data tensor, and updating the matching pattern in a process of the machine learning regarding the local dimension of the input data tensor.

Abstract: A non-transitory computer-readable recording medium stores therein a learning data selection program that causes a computer to execute a process including: extracting a first input data group relating to first input data in correspondence with designation of the first input data included in an input data group input to a machine learning model, the machine learning model classifying or determining transformed data that is transformed from input data; acquiring a first transformed data group of the machine learning model and a first output data group of the machine learning model, respectively, the first transformed data group being input to the machine learning model and corresponding to the first input data group, the first output data group corresponding to the first transformed data group; and selecting learning target data of an estimation model from the first input data group.

Abstract: A learning method includes: acquiring input data and correct answer information, the input data including a set of multiple pieces of relationship data in which relationships between variables are recorded respectively; determining conversion rule corresponding to each of the multiple pieces of relationship data such that relationships before and after a conversion of a common variable commonly in the multiple pieces of relationship data are the same, when converting a variable value in each of the multiple pieces of relationship data into converted data rearranging the variable values in an order of input; converting each of the multiple pieces of relationship data into a multiple pieces of the converted data according to each corresponding conversion rule; and inputting a set of the multiple pieces of converted data to the neural network and causing the neural network to learn a learning model based on the correct answer information.

Abstract: A machine learning method is performed by a computer. The method includes acquiring first graph information, generating second graph information, without changing a coupling state between nodes included in the first graph information, by a change process of changing an attribute value of a coupling between the nodes, and performing machine learning on a model, based on the first graph information and the second graph information.

Abstract: A non-transitory computer-readable recording medium has stored therein a program that causes a computer to execute a process, the process including determining numerical values indicating features at respective timings having a predetermined time interval with respect to time-series data to be analyzed, numbers of the numerical values at the respective timings being made same, and generating an attractor related to the time-series data based on the determined numerical values.

Abstract: A conversion method is performed by a computer. The method includes calculating, with respect to a core tensor and a factor matrix generated by decomposing tensor data, a rotational conversion matrix that reduces a value of an element included in the factor matrix, generating, based on the core tensor and an inverse rotational conversion matrix of the rotational conversion matrix, a core tensor after conversion obtained by converting the core tensor, and outputting the core tensor after conversion.

Abstract: A method includes: generating common information to be commonly applied to plural input data each including a combination of a value of each item and an input value in association with one or more items, the common information being for converting a correspondence between each input value and each input node in a machine learner in a case of inputting the plural input data to the machine learner; generating individual information to be individually applied to each input data, the individual information being for converting the correspondence, in association with a remaining item excluding the one or more items, by using a similarity between test data and collation data obtained by converting the correspondence; generating converted data obtained by converting the correspondence by using the generated common conversion information and the generated individual conversion information; and updating the collation data and the machine learner by using the generated converted data.

Abstract: A machine learning apparatus determines an order in which numerical values in an input dataset are to be entered to a neural network for data classification, based on a reference pattern that includes an array of reference values to provide a criterion for ordering the numerical values. The machine learning apparatus then calculates an output value of the neural network whose input-layer neural units respectively receive the numerical values arranged in the determined order. The machine learning apparatus further calculates an input error at the input-layer neural units, based on a difference between the calculated output value and a correct classification result indicated by a training label. The machine learning apparatus updates the reference values in the reference pattern, based on the input error at the input-layer neural units.

Abstract: A learning method implemented by a computer, includes: creating an input data tensor including a local dimension and a universal dimension by partitioning series data into local units, the series data including a plurality of elements, each of the plurality of elements in the series data being logically arranged in a predetermined order; and performing machine learning by using tensor transformation in which a transformation data tensor obtained by transforming the input data tensor with a transformation matrix is outputted using a neural network, wherein the learning includes rearranging the transformation matrix so as to maximize a similarity to a matching pattern serving as a reference in the tensor transformation regarding the universal dimension of the input data tensor, and updating the matching pattern in a process of the machine learning regarding the local dimension of the input data tensor.

Abstract: A data transformation apparatus selects items one by one and generates a first weight dataset and a second weight dataset on the basis of similarity between first records in a first dataset and second records in a second datasets. The first records and second records respectively include first item values and second item values that belong to the selected item. Based on the first weight dataset, the data transformation apparatus transforms the first dataset into a first similarity-determining dataset including third records. Each third record includes a numerical value that indicates a relationship between transformed item values belonging to different items. Further, based on the second weight dataset, the data transformation apparatus transforms the second dataset into a second similarity-determining dataset including fourth records. Each fourth record includes a numerical value that indicates a relationship between transformed item values belonging to different items.

Abstract: An apparatus identifies partial tensor data that contributes to machine learning using tensor data in a tensor format obtained by transforming training data having a graph structure. Based on the partial tensor data and the training data, the apparatus generates expanded training data to be used in the machine learning by expanding the training data.

Abstract: A learning method includes: acquiring input data and correct answer information, the input data including a set of multiple pieces of relationship data in which relationships between variables are recorded respectively; determining conversion rule corresponding to each of the multiple pieces of relationship data such that relationships before and after a conversion of a common variable commonly in the multiple pieces of relationship data are the same, when converting a variable value in each of the multiple pieces of relationship data into converted data rearranging the variable values in an order of input; converting each of the multiple pieces of relationship data into a multiple pieces of the converted data according to each corresponding conversion rule; and inputting a set of the multiple pieces of converted data to the neural network and causing the neural network to learn a learning model based on the correct answer information.

Abstract: A non-transitory computer-readable recording medium records an estimation program causing a computer to execute processing which includes: calculating a reconfiguration error from an input result value and a reconfiguration value that is estimated by a first estimator, which estimates a parameter value from a result value learned on a basis of past data, and a second estimator, which estimates a result value from a parameter value, by using a specific result value or a neighborhood result value in a neighborhood of the specific result value; searching for a first result value that minimizes a sum of a substitute error that is calculated from the input result value and the specific result value and the reconfiguration error; and outputting a parameter value that is estimated from the first result value by using the first estimator.

Abstract: A classification method executed by a computer for classifying a plurality of records into a plurality of groups, the classification method includes: acquiring the plurality of records, the plurality of records including a variable value respectively; tentatively classifying the plurality of records into the plurality of groups; calculating a commonality value indicating a degree of commonality of the variable value among the plurality of groups, based on the variable value included in each of the tentatively classified groups; classifying the plurality of records into the plurality of groups based on the commonality value; and outputting a result of the classifying.

Abstract: A non-transitory computer-readable recording medium stores therein a learning data selection program that causes a computer to execute a process including: extracting a first input data group relating to first input data in correspondence with designation of the first input data included in an input data group input to a machine learning model, the machine learning model classifying or determining transformed data that is transformed from input data; acquiring a first transformed data group of the machine learning model and a first output data group of the machine learning model, respectively, the first transformed data group being input to the machine learning model and corresponding to the first input data group, the first output data group corresponding to the first transformed data group; and selecting learning target data of an estimation model from the first input data group.