Abstract: A non-transitory computer-readable recording medium stores a program for causing a computer to execute a process, the process include identifying frequency components stronger than a predetermined reference among frequency components of time-series data, calculating values that indicate a relationship between one or more parameters used when generating a plurality of time-series features of the time-series data and periods having the identified frequency components, as features for the parameters, executing training of a first machine learning model by using importance of each of the time-series features on prediction that uses the time-series features and the features for each of the parameters to predict the importance of the time-series features from the features for each of the parameters, the time-series features being generated based on the parameters, and predicting importance of time-series features for new time-series data by using the trained first machine learning model.

Abstract: A search apparatus causes a first learning process using a first sample size and a first hyperparameter value to be executed, and causes a second learning process using the first sample size and a second hyperparameter value to be executed. When a third learning process using a second sample size larger than the first sample size and the first hyperparameter value has not been executed, the search apparatus calculates total resources associated with the first sample size based on resources used by the first and second learning processes. If the total resources exceed a threshold, the search apparatus allows the third learning process to be executed. If the total resources are equal to or less than the threshold, the search apparatus withholds the execution of the third learning process, and allows a fourth learning process using the first sample size or smaller and a third hyperparameter value to be executed.

Abstract: A program for causing a computer to execute processing including: generating division candidate datasets divided in accordance with different criteria from each other, from a combined dataset obtained by combining training data and validation data in a divided dataset that has been divided into the training data and the validation data used for machine learning; generating respective machine learning pipelines that execute machine learning, separately for each of the divided dataset and the division candidate datasets; using each of the divided dataset and the division candidate datasets to calculate respective prediction performances when the respective machine learning pipelines are executed; identifying division candidate datasets that have the prediction performances closest to the respective prediction performances calculated using the divided dataset, from among the division candidate datasets; and determining division criteria used for the identified division candidate dataset to be the division criter

Abstract: A non-transitory computer-readable recording medium stores a program for causing a computer to execute a process, the process includes obtaining first change information, which indicates a change in a feature of a first dataset when first preprocessing is performed on the first dataset, inputting the first change information to a trained machine learning model that outputs an inference result regarding preprocessing information that identifies each piece of second preprocessing for a second dataset, the trained machine learning model being trained by using training data in which the preprocessing information is associated with second change information that indicates a change in a feature of the second dataset when each piece of second preprocessing is performed, and identifying one or more pieces of recommended preprocessing that correspond to the first preprocessing based on the inference result that is output in response to the input of the first change information.

Abstract: A search point determining method in an estimation process of a function, executed by a processor included in a search point determining apparatus, the method includes, calculating a search prediction time and a confidence interval upper limit obtained by using a Gaussian process for the function in each search candidate point from a past search result of the function, generating an area in a parameter space for each search candidate point by using a position of a search point close to the relevant search candidate point in a past search result, a search prediction time corresponding to each search candidate point, and a confidence interval upper limit corresponding to each search candidate point, and determining a search point based on a size of the area in a plurality of parameter spaces.

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 machine learning management apparatus identifies a maximum prediction performance score amongst a plurality of prediction performance scores corresponding to a plurality of models generated by executing each of a plurality of machine learning algorithms. As for a first machine learning algorithm having generated a model corresponding to the maximum prediction performance score, the machine learning management apparatus determines a first training dataset size to be used when the first machine learning algorithm is executed next time based on the maximum prediction performance score, first estimated prediction performance scores, and first estimated runtimes.

Abstract: An allocation method executed by a computer includes dividing each of a plurality of pieces of time-series data into a plurality of segments, allocating a label to each of the pieces of time-series data based on features of each segment in the pieces of time-series data, and allocating a predetermined segment in time-series data, included in the pieces of time-series data, with a label allocated to the time-series data to which the predetermined segment belongs.

Abstract: A processor obtains a first measurement value representing prediction performance of a model that has been learned by using a first parameter value and training data of first size. The processor calculates a first expected value and a first variance degree of prediction performance of a model that would be learned by using the first parameter value and training data of second size. The processor also obtains a second measurement value representing prediction performance of a model that has been learned by using a second parameter value and training data of the first size. The processor calculates a second expected value and a second variance degree of prediction performance of a model that would be learned by using the second parameter value and training data of the second size.

Abstract: A rule presentation method by a computer, includes specifying a plurality of rules that specify one of examples according to the number of positive examples and the number of negative examples for one or more combinations of attributes, based on training data; acquiring first data that has a combination of attributes different from the combination of attributes included in the training data and is not associated with a label that designates the positive example or the negative example; selecting a rule related to the combination of attributes from among the plurality of specified rules; generating second data in which a label different from examples specified by the selected rule is associated with the first data; specifying the number of samples of the first data in which the label of the positive example or the negative example specified by the selected rule changes; and determining an order of rules.

Abstract: An allocation method executed by a computer includes dividing each of a plurality of pieces of time-series data into a plurality of segments, allocating a label to each of the pieces of time-series data based on features of each segment in the pieces of time-series data, and allocating a predetermined segment in time-series data, included in the pieces of time-series data, with a label allocated to the time-series data to which the predetermined segment belongs.

Abstract: A learning method executed by a computer, the learning method includes inputting a first data being a data set of transfer source and a second data being one of data sets of transfer destination to an encoder to generate first distributions of feature values of the first data and second distributions of feature values of the second data; selecting one or more feature values from among the feature values so that, for each of the one or more feature values, a first distribution of the feature value of the first data is similar to a second distribution of the feature value of the second data; inputting the one or more feature values to a classifier to calculate prediction labels of the first data; and learning parameters of the encoder and the classifier such that the prediction labels approach correct answer labels of the first data.

Abstract: A search point determining method in an estimation process of a function, executed by a processor included in a search point determining apparatus, the method includes, calculating a search prediction time and a confidence interval upper limit obtained by using a Gaussian process for the function in each search candidate point from a past search result of the function, generating an area in a parameter space for each search candidate point by using a position of a search point close to the relevant search candidate point in a past search result, a search prediction time corresponding to each search candidate point, and a confidence interval upper limit corresponding to each search candidate point, and determining a search point based on a size of the area in a plurality of parameter spaces.

Abstract: A search apparatus causes a first learning process using a first sample size and a first hyperparameter value to be executed, and causes a second learning process using the first sample size and a second hyperparameter value to be executed. When a third learning process using a second sample size larger than the first sample size and the first hyperparameter value has not been executed, the search apparatus calculates total resources associated with the first sample size based on resources used by the first and second learning processes. If the total resources exceed a threshold, the search apparatus allows the third learning process to be executed. If the total resources are equal to or less than the threshold, the search apparatus withholds the execution of the third learning process, and allows a fourth learning process using the first sample size or smaller and a third hyperparameter value to be executed.

Abstract: A machine learning management apparatus identifies a maximum prediction performance score amongst a plurality of prediction performance scores corresponding to a plurality of models generated by executing each of a plurality of machine learning algorithms. As for a first machine learning algorithm having generated a model corresponding to the maximum prediction performance score, the machine learning management apparatus determines a first training dataset size to be used when the first machine learning algorithm is executed next time based on the maximum prediction performance score, first estimated prediction performance scores, and first estimated runtimes.

Abstract: A machine learning management apparatus identifies a maximum prediction performance score amongst a plurality of prediction performance scores corresponding to a plurality of models generated by executing each of a plurality of machine learning algorithms. As for a first machine learning algorithm having generated a model corresponding to the maximum prediction performance score, the machine learning management apparatus determines a first training dataset size to be used when the first machine learning algorithm is executed next time based on the maximum prediction performance score, first estimated prediction performance scores, and first estimated runtimes.

Abstract: A processor obtains a first measurement value representing prediction performance of a model that has been learned by using a first parameter value and training data of first size. The processor calculates a first expected value and a first variance degree of prediction performance of a model that would be learned by using the first parameter value and training data of second size. The processor also obtains a second measurement value representing prediction performance of a model that has been learned by using a second parameter value and training data of the first size. The processor calculates a second expected value and a second variance degree of prediction performance of a model that would be learned by using the second parameter value and training data of the second size.

Abstract: A machine learning management device executes each of a plurality of machine learning algorithms by using training data. The machine learning management device calculates, based on execution results of the plurality of machine learning algorithms, increase rates of prediction performances of a plurality of models generated by the plurality of machine learning algorithms, respectively. The machine learning management device selects, based on the increase rates, one of the plurality of machine learning algorithms and executes the selected machine learning algorithm by using other training data.