Abstract: A learning classification device includes a data memory, a learning unit, and a classification unit. The data memory is configured to store training data for learning a decision tree. The learning unit is configured to read a plurality of feature quantities included in the training data from the data memory by single access and derive data of a node based on the plurality of feature quantities, to learn the decision tree. The classification unit is configured to determine where the training data read from the data memory is to be split to from the node, based on the data of the node derived by the learning unit.

Abstract: An information processing apparatus that is capable of reducing time and effort to set settings of a smart speaker that cooperates with the information processing apparatus when a user starts to use the smart speaker. The information processing apparatus acquires identification information of the user, and acquires audio control information associated with the acquired identification information. Then, the information processing apparatus requests the smart speaker to change the audio setting of the smart speaker based on the acquired audio control information.

Abstract: A measurement apparatus includes a total reflection member configured to totally reflect an incoming probe beam in a state being in contact with a measured object; and a temperature adjuster configured to maintain, to a predetermined temperature, a temperature of a contact region of the total reflection member with the measured object.

Abstract: A data transmission apparatus includes a setting unit configured to set a plurality of settings for restricting execution of transmission processing causing the data transmission apparatus to transmit data to an area outside a predetermined area. The setting unit sets the plurality of settings based on a simple operation executed by a user.

Abstract: A learning device is configured to perform learning by gradient boosting, and includes: data memory units configured to store learning data including a type of feature amount and corresponding gradient information; gradient output units each configured to receive an input of the feature amount and the corresponding gradient information from a corresponding one of the data memory units, and output the gradient information through an output port corresponding to each value of the input feature amount; an addition unit configured to add up one or more pieces of the gradient information corresponding to the same value of the feature amount, and output an added value of the gradient information corresponding to each value of the feature amount; and a histogram memory unit configured to store a histogram obtained by integrating added values of the gradient information corresponding to each value of the feature amount as a bin.

Abstract: A learning device includes a learning unit configured to learn a model using learning data stored in a data storage unit; a subsampling unit configured to determine whether to use the learning data corresponding to gradient information updated by an update unit based on the model, for learning of a next model after learning of one model based on a subsampling rate; first and second buffer unit configured to buffer learning data determined to be used for and data determined not to be used for learning of the next model and gradient information corresponding to the learning data, respectively, up to a volume determined in advance. The first buffer unit and the second buffer unit are configured to write the learning data and the gradient information into the data storage unit for each predetermined block when buffering the learning data and the gradient information up to the volume.

Abstract: An image forming apparatus includes: an image forming device configured to form an image on a sheet; a fan configured to be driven to perform ventilation; and at least one controller configured to function as: a unit configured to store flag information indicating a state where a dew condensation countermeasure is required in a non-volatile memory; a unit configured to restrict image formation by the image forming device and execute dew condensation countermeasure processing to cause the fan to perform predetermined driving; and a unit configured to delete, from the non-volatile memory, the flag information upon completion of the dew condensation countermeasure processing, wherein the controller executes the dew condensation countermeasure processing upon activation of the image forming apparatus based on at least storage of the flag information in the non-volatile memory.

Abstract: A learning device includes a data storage unit configured to store learning data for learning a decision tree; a learning unit configured to determine whether to cause learning data stored in the data storage unit to branch to one node or to the other node of lower nodes of a node based on a branch condition for the node of the decision tree; and a first buffer unit and a second buffer unit configured to buffer learning data determined to branch to the one node and the other node, respectively, by the learning unit up to capacity determined in advance. The first buffer unit and the second buffer unit are configured to, in response to buffering learning data up to the capacity determined in advance, write the learning data into continuous addresses of the data storage unit for each predetermined block.

Abstract: An apparatus and a method are disclosed, each of which applies a plurality of different spatial filters to one input image to generate a plurality of filtered images; calculates, for each of a plurality of pixels included in each of the plurality of filtered image, a score indicating a value determined by a difference from a corresponding one of a plurality of model groups, using the plurality of model groups that respectively correspond to the plurality of filtered images and each including one or more models having a parameter representing a target shape; calculates an integrated score indicating a result of integrating the scores of the respective plurality of pixels corresponding to each other over the plurality of filtered images; and determines an abnormality based on the integrated score.

Abstract: A measuring apparatus includes a light source configured to output light in a mid-infrared region, a detector configured to irradiate a measuring object with the light output from the light source and detect reflected light reflected by the measuring object, and a blood glucose level measuring device configured to measure a blood glucose level of the measuring object. A wavenumber between a plurality of absorption peak wavenumbers of glucose is used as a blood glucose level measuring wavenumber for measuring the blood glucose level.

Abstract: According to one aspect of the present invention, a learning device is configured to perform learning of decision trees by gradient boosting. The learning device includes an initializer and a learning unit. The initializer is configured to perform initialization processing on an address memory that stores an address in a data memory of learning data used for learning of a node in a decision tree. The learning unit is configured to perform learning of the decision tree by using the learning data stored in the data memory. The initializer is configured to perform, after a certain decision tree is learned by the learning unit, the initialization processing for a next decision tree of the certain decision tree in parallel with processing for the certain decision tree.

Abstract: A learning device is configured to perform learning of a decision tree. The learning device includes a plurality of learning units and a plurality of performance calculators. The plurality of learning units are configured to perform learning of the decision tree using learning data divided into pieces to be stored in a plurality of data memories. The plurality of performance calculators are each configured to calculate an index value of index values for each of the plurality of data memories, the index value indicating recognition performance of the decision tree learned by corresponding one of the plurality of learning units.

Abstract: A learning device is configured to perform learning of a decision tree by gradient boosting. The learning device includes a plurality of learning units and a plurality of model memories. The plurality of learning units are configured to perform learning of the decision tree using learning data divided to be stored in a plurality of data memories. The plurality of model memories are each configured to store data of the decision tree learned by corresponding one of the plurality of learning units.

Abstract: A learning device includes: a learning unit configured to read out feature amounts of learning data from a data memory and derive a branch condition for a node of a decision tree based on the feature amounts, to perform learning of the decision tree; and a discriminator configured to perform determining, in accordance with the branch condition, a node to which learning data is to be branched from the node corresponding to the branch condition.

Abstract: An information processing apparatus and method are disclosed, each of which: using a set of normal data, learns a first model for determining the normal data; sets, out of a plurality of abnormality candidate areas, the abnormality candidate areas selected by a user as correct data and the abnormality candidate areas not selected by the user as incorrect data, to learn a second model for identifying the correct data and the incorrect data, each abnormality candidate area indicating a candidate area of an abnormality and detected based on the first model from each of a plurality of captured images; obtains the captured images; detects the abnormality candidate areas from the respective captured images, using the first model; determines whether the abnormality candidate areas detected belong to the correct data or the incorrect data, using the second model; and controls to output a determination.

Abstract: A learning device is configured to perform learning of a decision tree. The learning device includes a branch score calculator, and a scaling unit. The branch score calculator is configured to calculate a branch score used for determining a branch condition for a node of the decision tree based on a cumulative sum of gradient information corresponding to each value of a feature amount of learning data. The scaling unit is configured to perform scaling on a value related to the cumulative sum used for calculating the branch score by the branch score calculator to fall within a numerical range with which the branch score is capable of being calculated.

Abstract: A learning device is configured to perform learning of a decision tree, and includes: a plurality of learning units each corresponding to a data memory of a plurality of data memories, and being configured to perform learning at a first node using learning data acquired by using first addresses related to a storage destination of the learning data corresponding to the first node of the decision tree in the data memory, and output a second address related to a storage destination of each piece of the learning data branched from the first node; and a plurality of managers each corresponding to a learning unit of the plurality of learning units, and being configured to calculate third addresses related to storage destinations of learning data corresponding to second nodes being next nodes of the first node using the first addresses and the second address output from the learning unit.

Abstract: A learning device is configured to perform learning of a decision tree. The learning device includes a gradient output unit and a branch score calculator. The gradient output unit is configured to output a cumulative sum of gradient information corresponding to each value of a feature amount of learning data. The branch score calculator is configured to calculate a branch score used for determining a branch condition for a node of the decision tree, from the cumulative sum without using a dividing circuit.

Abstract: A driving determination device includes an acquirer configured to acquire at least a captured image of a driving body in a driving direction and information that changes with movement of the driving body; a driving level calculator configured to calculate a driving level for evaluating a driving method for the driving body for each predetermined determination item, using at least one of the acquired captured image and the acquired information that changes with the movement of the driving body; an itemized calculator configured to calculate values based on a plurality of the calculated driving levels for each determination item; and an evaluation result calculator configured to calculate a value for comprehensively evaluating the driving method for the driving body, using the values based on the driving levels for each determination item.

Abstract: A learning and discrimination device configured to perform learning by gradient boosting, including: a histogram memory unit configured to store, for each value of a feature amount, a histogram obtained by integrating gradient information corresponding to the value in sample data; a category accumulated gradient calculator configured to calculate a histogram corresponding to a value of the feature amount corresponding to a threshold for the feature amount as a cumulative sum of the gradient information; a non-category accumulated gradient calculator configured to calculate, as a cumulative sum of the gradient information, a sum of a histogram corresponding to a value of the feature amount that is classified using a classification method with the threshold different from the category accumulated gradient calculator; and a cumulative sum selector configured to output the cumulative sum calculated by the category accumulated gradient calculator or the non-category accumulated gradient calculator.