Abstract: The information processing apparatus generates a second model by updating, while fixing parameters of first one or more layers corresponding to a first position in a first model, parameters of second one or more layers corresponding to a second position in the first model, based on a loss function including entropy of a first output outputted from the first model in response to an input of first data to the first model, the first data being data that does not include correct labels; and generates a third model by updating, while fixing parameters of third one or more layers corresponding to the second position in the second model, parameters of fourth one or more layers corresponding to the first position, based on a loss function including entropy of a second output outputted from the second model in response to the input of the first data to the second model.

Abstract: The computer is caused to execute processing including: generating a second machine learning model, by updating a parameter of a first machine learning model, based on a first feature amount that is obtained from first information using the parameter of the first machine learning model; generating a third machine learning model, based on a first training data and a second training data, the first training data including: a second feature amount that is obtained from a second data based on a parameter of the second machine learning model; and a correct label indicating first information, the second training data including: a third feature amount that is obtained from a third data based on the parameter of the second machine learning model; and a correct label indicating second information; evaluating the second machine learning model, based on the prediction accuracy of the generated third machine learning model.

Abstract: A recording medium stores a program for causing a computer to execute a process including: classifying data into classes based on a density of the data; performing data augmentation on first data that is positioned in a region where data which is positioned in a region of a first class and which belongs to the first class exists at a higher density than a predetermined density and on second data that is positioned in a region where the data which is positioned in the region of the first class and which belongs to the first class exists at a lower density than the predetermined density; and setting, when the first data after the data augmentation and the second data after the data augmentation overlap each other, a label that corresponds to the first class to first augmentation data, the second data, or second augmentation data.

Abstract: A recording medium stores a machine learning program causing a computer to execute a processing of: generating a first parameter relating to a first pruning process that generates a first machine learning model to classify a first class in classes by executing the first pruning process on a machine learning model which classifies into the classes based on a parameter of the machine learning model and training data including the first class which serves a correct answer label; and generating a second parameter relating to a second pruning process that generates a second machine learning model to classify a second class in the classes by executing the second pruning process on the machine learning model based on the parameter of the machine learning model, training data including the second class which serves the correct answer label and a loss function including the first parameter relating to the first pruning process.

Abstract: A non-transitory computer-readable recording medium stores a detection program for causing a computer to execute processing including: inputting a plurality of pieces of second data into a second machine learning model generated by machine learning based on a plurality of pieces of first data and a first result output from a first machine learning model according to an input of the plurality of pieces of first data; acquiring a second result output from the second machine learning model according to the input of the plurality of pieces of second data; and detecting a difference between a distribution of the plurality of pieces of first data and a distribution of the plurality of pieces of second data, based on comparison between a value calculated based on the second result and a gradient of a loss function of the second machine learning model with a threshold.

Abstract: A prediction method implemented by a computer, the method includes: receiving a classification model from a server, the classification model being a model for classifying logs of an electronic device into two or more classes, the server being a computer configured to distribute the classification model; calculating, with respect to different time points, a prediction error by using a predicted value outputted by the classification model and an actual measured value observed at each of the different time points; performing sequential machine learning for the classification model to have the prediction error satisfy a certain condition; and when a cumulative sum with respect to the prediction error of the sequential machine learning is equal to or greater than a threshold, requesting the server apparatus to relearn the classification model.

Abstract: A computing system trains a machine learning by using a plurality of pieces of training data including first data associated with a first class and second data associated with a second class. The computing system trains an inspector model for training a decision boundary between an area of the first class and an area of the second class based on knowledge distillation of the operation model, the inspector model being constructed for calculating a distance from the decision boundary to operation data. The computing system detects, based on a result obtained by inputting the plurality of pieces of training data and a plurality of pieces of data to the inspector model, a change in an output result of the operation model caused by a difference between training data and data.

Abstract: A computing system calculates, by using an inspector model, whether or not the plurality of pieces of training data are located in a vicinity of the decision boundary, acquires a first proportion of the training data, calculates, by using the inspector model, whether or not a plurality of pieces of operation data associated with one of correct answer labels out of the plurality of correct answer labels are located in a vicinity of the decision boundary, and acquires a second proportion of the operation data located in the vicinity of the decision boundary out of all of the pieces of operation data and detects, based on the first proportion and the second proportion, a change in the output result of the machine learning model caused by a temporal change in a tendency of the operation data.

Abstract: A computing system trains an inspector model for training a decision boundary that divides a feature space of data into two application areas based on an output result of the operation model, the inspector model being configured to calculate a distance from the decision boundary to input data. The computing system calculates, by inputting training data to the inspector model, a first distance from the decision boundary to the training data. The computing system calculates, by inputting first data to the inspector model, a second distance from the decision boundary to the operation data. The computing system detects, when a difference between the first distance and the second distance is larger than or equal to a threshold, an accuracy degradation of the machine learning model caused according to the difference between the training data and the first data.

Abstract: A prediction method implemented by a computer, the method includes: receiving a classification model from a server, the classification model being a model for classifying logs of an electronic device into two or more classes, the server being a computer configured to distribute the classification model; calculating, with respect to different time points, a prediction error by using a predicted value outputted by the classification model and an actual measured value observed at each of the different time points; performing sequential machine learning for the classification model to have the prediction error satisfy a certain condition; and when a cumulative sum with respect to the prediction error of the sequential machine learning is equal to or greater than a threshold, requesting the server apparatus to relearn the classification model.