Abstract: Even when data that can belong to a new class that is not in an existing class is input, this data can be easily classified appropriately. Classification system includes input reception part, classification part, calculation part, determination part, and presentation part. Input reception part receives an input of target data. Classification part classifies the target data into any one of a plurality of classes. Calculation part calculates a feature amount of the target data. Determination part determines a possibility that the target data is classified into the new class based on a classification result in classification part and the feature amount of the target data calculated by calculation part. When determination part determines that there is a possibility that the target data is classified into the new class, presentation part presents a determination result of determination part.

Abstract: Reliability regarding a class determination for an object is improved. Classification system includes first classification part, second classification part, and determination part. First classification part classifies first target data into at least one of a plurality of first classes. Second classification part classifies second target data into at least one of a plurality of second classes. Determination part decides whether to use one or both of a first classification result that is a classification result obtained by first classification part and a second classification result that is a classification result obtained by second classification part, and determines a class of object based on one or both of them. The first target data is image data of object. The second target data is manufacturing data regarding a manufacturing condition of object.

Abstract: First, a plurality of models that predict categories of input data are pooled. At least one of the plurality of models is a model trained by machine learning. Next, each of a plurality of hybrid model candidates that judge the categories are created by selecting and combining two or more models from among the plurality of pooled models. Then, by comparing the plurality of hybrid model candidates, one of the plurality of hybrid model candidates is selected as a hybrid model.

Abstract: An inspection system includes an acquisition unit and a determination unit. The acquisition unit acquires an image representing a surface of an object. The determination unit performs color determination processing. The color determination processing is performed to determine a color of the surface of the object based on a plurality of conditions of reflection. The plurality of conditions of reflection are obtained from the image representing the surface of the object as acquired by the acquisition unit, and have a specular reflection component and a diffuse reflection component at respectively different ratios on the surface of the object.

Abstract: Reliability regarding a class determination for an object is improved. Classification system includes first classification part, second classification part, and determination part. First classification part classifies first target data into at least one of a plurality of first classes. Second classification part classifies second target data into at least one of a plurality of second classes. Determination part decides whether to use one or both of a first classification result that is a classification result obtained by first classification part and a second classification result that is a classification result obtained by second classification part, and determines a class of object based on one or both of them. The first target data is image data of object. The second target data is manufacturing data regarding a manufacturing condition of object.

Abstract: An inspection system includes an acquisition unit and a determination unit. The acquisition unit acquires an image representing a surface of an object. The determination unit performs color determination processing. The color determination processing is performed to determine a color of the surface of the object based on a plurality of conditions of reflection. The plurality of conditions of reflection are obtained from the image representing the surface of the object as acquired by the acquisition unit, and have a specular reflection component and a diffuse reflection component at respectively different ratios on the surface of the object.

Abstract: Even when data that can belong to a new class that is not in an existing class is input, this data can be easily classified appropriately. Classification system includes input reception part, classification part, calculation part, determination part, and presentation part. Input reception part receives an input of target data. Classification part classifies the target data into any one of a plurality of classes. Calculation part calculates a feature amount of the target data. Determination part determines a possibility that the target data is classified into the new class based on a classification result in classification part and the feature amount of the target data calculated by calculation part. When determination part determines that there is a possibility that the target data is classified into the new class, presentation part presents a determination result of determination part.

Abstract: An inspection system includes an acquisition unit and a determination unit. The acquisition unit acquires an image representing a surface of an object. The determination unit performs color determination processing. The color determination processing is performed to determine a color of the surface of the object based on a plurality of conditions of reflection. The plurality of conditions of reflection are obtained from the image representing the surface of the object as acquired by the acquisition unit, and have a specular reflection component and a diffuse reflection component at respectively different ratios on the surface of the object.

Abstract: An inspection system includes an acquisition unit and a determination unit. The acquisition unit acquires an image representing a surface of an object. The determination unit performs color determination processing. The color determination processing is performed to determine a color of the surface of the object based on a plurality of conditions of reflection. The plurality of conditions of reflection are obtained from the image representing the surface of the object as acquired by the acquisition unit, and have a specular reflection component and a diffuse reflection component at respectively different ratios on the surface of the object.

Abstract: An information processing system receives first travel histories from vehicles that belong to vehicle type A, learns based on the first travel histories to build a first driver model that represents relation between travel situations and behaviors of the vehicles that belong to a first vehicle type, receives second travel histories from vehicles that belong to vehicle type X that is different from vehicle type A, and performs transfer learning in which the second travel histories are used for the first driver model to build a second driver model that represents relation between travel situations and behaviors of the vehicles that belong to vehicle type X.

Abstract: An information processing system receives first travel histories from vehicles that belong to vehicle type A, learns based on the first travel histories to build a first driver model that represents relation between travel situations and behaviors of the vehicles that belong to a first vehicle type, receives second travel histories from vehicles that belong to vehicle type X that is different from vehicle type A, and performs transfer learning in which the second travel histories are used for the first driver model to build a second driver model that represents relation between travel situations and behaviors of the vehicles that belong to vehicle type X.

Abstract: A muscle fatigue output device is provided with a myoelectric measurement unit that acquires myoelectricity of a user, and a main control unit that determines fatigue of a muscle of the user on the basis of the myoelectricity. The main control unit (a) uses the myoelectricity to acquire a value for a frequency characteristic of the myoelectricity, (b) uses the myoelectricity to acquire a value for an amplitude characteristic of the myoelectricity, (c) acquires a ratio between the value for the frequency characteristic and the value for the amplitude characteristic as an index for the fatigue of the muscle of the user, and (d) outputs information regarding the fatigue of the muscle of the user, on the basis of the index for the fatigue of the muscle of the user.

Abstract: A vehicle-mounted interface device includes a receiver and a determiner. The receiver receives an input from a driver who drives a vehicle. On the basis of the input received by the receiver, the determiner determines whether or not the driver is familiar with a route along which the driver is to drive or is driving the vehicle.

Abstract: An individual authentication method includes (i) indicating a first position of a user's arm or arms when gripping an electrocardiographic sensor, (ii) measuring the user's electrocardiographic activity at the first position by using the electrocardiographic sensor, (iii) indicating a second position of the user's arm or arms when gripping the electrocardiographic sensor, the second position being different from the first position, (iv) measuring the user's electrocardiographic activity at the second position by using the electrocardiographic sensor, (v) receiving ID information of the user, and (vi) registering, in a database, electrocardiographic authentication information including first authentication information associating the ID information with the user's electrocardiographic activity measured at the first position, and second authentication information associating the ID information with the user's electrocardiographic activity measured at the second position.

Abstract: A muscle fatigue output device is provided with a myoelectric measurement unit that acquires myoelectricity of a user, and a main control unit that determines fatigue of a muscle of the user on the basis of the myoelectricity. The main control unit (a) uses the myoelectricity to acquire a value for a frequency characteristic of the myoelectricity, (b) uses the myoelectricity to acquire a value for an amplitude characteristic of the myoelectricity, (c) acquires a ratio between the value for the frequency characteristic and the value for the amplitude characteristic as an index for the fatigue of the muscle of the user, and (d) outputs information regarding the fatigue of the muscle of the user, on the basis of the index for the fatigue of the muscle of the user.

Abstract: A training apparatus includes a display that displays a video image including an image of a moving object, a rotation speed acquirer that acquires a rotation speed of a pedal driven by a user, and a control circuit that changes a moving speed of the object in the video image. The control circuit controls the moving speed as follows. The moving speed of the object in the video image is increased at an acceleration ?1 (>0). If an increase in the pedal rotation speed occurs after the moving speed of the object is increased, the moving speed of the object is increased at an acceleration ?2 (<?1). In a case where an increase in the rotation speed of the pedal occurs after the moving speed of the object is increased at the acceleration ?2, the moving speed of the object is further continuously increased at the acceleration ?2.

Abstract: An individual authentication method includes (i) indicating a first position of a user's arm or arms when gripping an electrocardiographic sensor, (ii) measuring the user's electrocardiographic activity at the first position by using the electrocardiographic sensor, (iii) indicating a second position of the user's arm or arms when gripping the electrocardiographic sensor, the second position being different from the first position, (iv) measuring the user's electrocardiographic activity at the second position by using the electrocardiographic sensor, (v) receiving ID information of the user, and (vi) registering, in a database, electrocardiographic authentication information including first authentication information associating the ID information with the user's electrocardiographic activity measured at the first position, and second authentication information associating the ID information with the user's electrocardiographic activity measured at the second position.