Abstract: A computer acquires cluster data and performance data. The cluster data represents classification results obtained by classifying multiple sample programs into two or more clusters and arranging the clusters in multiple levels in such a manner that each level contains a different number of clusters. The performance data represents an execution performance of each sample program. The computer calculates, for each of two or more clusters in each level, a first evaluation value based on an index value for reusability of two or more sample programs belonging to the cluster and the execution performances of those sample programs. The computer also calculates, for each level, a second evaluation value based on the first evaluation values corresponding to the two or more clusters of the level. The computer selects, based on the second evaluation values corresponding to the multiple levels, the classification results of a level amongst the multiple levels.

Abstract: A computer acquires a plurality of clustering results, each of which differs in the number of clusters, by performing clustering that classifies a plurality of sample programs into two or more clusters based on features associated with description and an execution performance of each sample program. The computer calculates, for each of the two or more clusters in each of the clustering results, a first evaluation value based on an index value for reusability of sample programs included in the cluster and the execution performances of the sample programs. The computer calculates, for each of the clustering results, a second evaluation value based on two or more of the first evaluation values corresponding to the two or more clusters. The computer selects, based on the second evaluation values corresponding to the clustering results, one clustering result amongst the multiple clustering results.

Abstract: A computer acquires cluster data and performance data. The cluster data represents classification results obtained by classifying multiple sample programs into two or more clusters and arranging the clusters in multiple levels in such a manner that each level contains a different number of clusters. The performance data represents an execution performance of each sample program. The computer calculates, for each of two or more clusters in each level, a first evaluation value based on an index value for reusability of two or more sample programs belonging to the cluster and the execution performances of those sample programs. The computer also calculates, for each level, a second evaluation value based on the first evaluation values corresponding to the two or more clusters of the level. The computer selects, based on the second evaluation values corresponding to the multiple levels, the classification results of a level amongst the multiple levels.

Abstract: A non-transitory computer-readable recording medium stores a machine learning program for causing a computer to execute processing including: measuring, for each data, a non-functional performance that represents a performance for a requirement that excludes a function of each data; and by machine learning that uses divided data obtained by dividing each data into a first portion of the data and a second portion that is correct answer data as training data, executing machine learning processing of training a prediction model that predicts the second portion of the data in response to an input of the first portion of the data, wherein the machine learning processing uses a loss function that includes a parameter determined according to a measurement result of the non-functional performance that is the parameter that indicates a ratio of reflecting the non-functional performance in the prediction model.

Abstract: A non-transitory computer-readable recording medium storing an information processing program for causing a computer to execute processing, the processing including: acquiring features that represents contents of each of programs of a program group related to a certain field; acquiring a performance value that represents performance of each of the programs; classifying the program group into a plurality of clusters on the basis of the acquired features; selecting, from each cluster of the plurality of clusters, one or more of programs that have relatively great acquired performance values; and outputting the one or more of selected programs.

Abstract: A recording medium stores a program for causing a computer to execute a process including: calculating a first embedded vector for each cluster obtained by clustering samples in training data, by inputting the samples that represent the clusters to a first distance metric model; performing training of a second distance metric model, based on labels set in pairs of the samples; calculating a second embedded vector for each cluster, by inputting the samples that represent the clusters to the second distance metric model; detecting pairs of the clusters that are likely to be integrated when the training is performed with a greater number of epochs than a number of epochs at a time of the training of the second distance metric model, based on the first embedded vector and the second embedded vector; and outputting one of the pairs of the clusters in which a similarity label is not set.

Abstract: A non-transitory computer-readable storage medium storing a program that causes a computer to execute a process, the process includes learning distributed representations of words included in a word space of a first language using a learner for learning the distributed representations; classifying words included in a word space of a second language different from the first language into words common to words included in the word space of the first language and words not common to words included in the word space of the first language; and replacing distributed representations of the common words included in the word space of the second language with distributed representations of the words, corresponding to the common words, in the first language and adjusting a parameter of the learner.

Abstract: A clustering method, clustering program, and clustering device are described herein for clustering of words with multiple meanings based on generating vectors for each meaning of a word. The generated vectors provide a distributed representation of a word in a vector space that account for the multiple meanings of the word so as, for instance, to learn semantic representations with high accuracy.

Abstract: A computer-readable recording medium recording at analysis program for causing a computer to execute processing includes: measuring a diversity degree of each word included in a document which is used for learning; classifying the each word into a first word group of which the diversity degree is higher than a specified value and a second word group of which the diversity degree is lower than the specified value; learning a first distributed representation of a word included in the first word group by using a first learning algorithm to learn a distributed representation; when a word which is used by a second learning algorithm to learn the distributed representation is included in the first word group, setting a third distributed representation of the word to the second learning algorithm; and learning a second distributed representation of a word included in the second word group by using the second learning algorithm.

Abstract: A learning device includes a memory and a processor configured to execute a process including acquiring a plurality of documents, clustering the plurality of documents with respect to each of a first plurality of words, the first plurality of words being included in the plurality of documents, assigning a common label to a first word and a second word among the first plurality of words in a case where a cluster relating to the first word and a cluster relating to the second word resemble each other, and re-clustering, on the basis of the common label, the plurality of documents including the first word and the second word after the assigning the common label.

Abstract: A learning apparatus includes a memory and a processor configured to acquire a plurality of documents, perform clustering of the plurality of documents for each of a plurality of words included in the plurality of document, when a plurality of clusters are generated for a first word among the plurality of words by the clustering, perform assignment of different labels corresponding to the plurality of clusters to the first word included in the plurality of documents, and perform re-clustering of the plurality of documents including the first word with the assigned different labels, for other words among the plurality of words.

Abstract: A selection method executed by a processor included in a selection apparatus, the selection method includes when plurality of pieces of data are each determined as one of multiple determination candidates by using a learning model, calculating, for each of the plurality of pieces of data, a deviation index indicating a degree of uncertainty of a determination result obtained by using the learning model with respect to each of the multiple determination candidates; and when the learning model is updated, responsively selecting a particular unit of data targeted for redetermination to be performed by using the updated learning model from the plurality of pieces of data in accordance with the deviation index.

Abstract: A clustering method performed by a computer for clustering on a plurality of elements given relationship data concerning the relationship between some elements, the method includes: calculating relevance between the plurality of elements by using the attributes of the plurality of elements; calculating a threshold value for identifying link attributes between the elements in accordance with the relevance and the relationship data concerning each set of elements given the relationship data; determining link types between the plurality of elements in accordance with the threshold value; and performing clustering in accordance with the result of determination.

Abstract: A clustering method for generating a distributed representation of a word in a vector space, the method includes: recording, before a learning of multiple words, a first vector value in the vector space for each of the multiple words; recording, after the learning, a second vector value in the vector space for each of the multiple words; clustering the multiple words based on a change between the first vector value and the second vector value; and generating vectors separated for each meaning with respect to a word with multiple meanings included among the multiple words, based on a result of the clustering.

Abstract: A computer-readable recording medium recording at analysis program for causing a computer to execute processing includes: measuring a diversity degree of each word included in a document which is used for learning; classifying the each word into a first word group of which the diversity degree is higher than a specified value and a second word group of which the diversity degree is lower than the specified value; learning a first distributed representation of a word included in the first word group by using a first learning algorithm to learn a distributed representation; when a word which is used by a second learning algorithm to learn the distributed representation is included in the first word group, setting a third distributed representation of the word to the second learning algorithm; and learning a second distributed representation of a word included in the second word group by using the second learning algorithm.

Abstract: A non-transitory computer-readable storage medium storing a program that causes a computer to execute a process, the process includes learning distributed representations of words included in a word space of a first language using a learner for learning the distributed representations; classifying words included in a word space of a second language different from the first language into words common to words included in the word space of the first language and words not common to words included in the word space of the first language; and replacing distributed representations of the common words included in the word space of the second language with distributed representations of the words, corresponding to the common words, in the first language and adjusting a parameter of the learner.

Abstract: A control unit calculates a first document count indicating the number of documents that are retrieved based on a first keyword, a second document count indicating the number of documents that are retrieved based on a logical AND of the first keyword and a second keyword belonging to one item, and a third document count indicating the number of documents that are retrieved based on a logical AND of the first keyword and a third keyword belonging to the one item, from the set of documents. The control unit calculates an evaluation value of the one item, based on the first, second, and third document counts. When the evaluation value satisfies a predetermined condition, the control unit outputs recommendation information recommending use of the one item to narrow down documents that are retrieved.

Abstract: A learning apparatus includes a memory and a processor configured to acquire a plurality of documents, perform clustering of the plurality of documents for each of a plurality of words included in the plurality of document, when a plurality of clusters are generated for a first word among the plurality of words by the clustering, perform assignment of different labels corresponding to the plurality of clusters to the first word included in the plurality of documents, and perform re-clustering of the plurality of documents including the first word with the assigned different labels, for other words among the plurality of words.

Abstract: A learning device includes a memory and a processor configured to execute a process including acquiring a plurality of documents, clustering the plurality of documents with respect to each of a first plurality of words, the first plurality of words being included in the plurality of documents, assigning a common label to a first word and a second word among the first plurality of words in a case where a cluster relating to the first word and a cluster relating to the second word resemble each other, and re-clustering, on the basis of the common label, the plurality of documents including the first word and the second word after the assigning the common label.

Abstract: A control unit extracts a plurality of potential features each included in at least one of a plurality of teacher data elements, from the plurality of teacher data elements. The control unit calculates the degree of importance of each potential feature in machine learning on the basis of the frequency of occurrence of the potential feature in the teacher data elements. The control unit calculates the information amount of each teacher data element on the basis of the degrees of importance of the potential features included in the teacher data element. The control unit selects teacher data elements for use in the machine learning from the teacher data elements on the basis of the information amounts of the respective teacher data elements.