Abstract: An information processing apparatus includes a network learning portion that performs learning of an appearance/position recognition network by constraining first to third weights and using a learning image, wherein the appearance/position recognition network has a foreground layer including a position node, a background layer including a background node, and an image layer including a pixel node, and is a neural network in which the position node, the background node, and the pixel node are connected to each other, and wherein the first weight is a connection weight between the position node and the pixel node, the second weight is a connection weight between the position node and the background node, and the third weight is a connection weight between the background node and the pixel node.

Abstract: An information input apparatus includes an observation unit that observes an environment including a user and one or more apparatuses to be controlled and includes a sensor; a learning unit that separates a foreground including the user and the one or more apparatuses to be controlled and a background including the environment except for the foreground from observation data obtained by the observation unit and learns three-dimensional models of the foreground and the background; a state estimation unit that estimates positions and postures of already modeled foregrounds in the environment; a user recognition unit that identifies fingers of the user from the foreground and recognizes a shape, position, and posture of the fingers; and an apparatus control unit that outputs a control command to the one or more apparatuses to be controlled on the basis of the recognized shape, position, and posture of the fingers.

Abstract: An information processing device includes: a foreground state estimating unit configured to estimate a foreground state of an image using an actual image which is an image to be actually observed; and a visible model updating unit configured to update a background visible model which is visibility of the background of an image and a foreground visible model which is visibility of the foreground using an estimation result of the foreground state.

Abstract: An information processing device includes a learning unit that performs, using an action performed by an object and an observation value of an image as learning data, learning of a separation learning model that includes a background model that is a model of the background of the image and one or more foreground model(s) that is a model of a foreground of the image, which can move on the background, in which the background model includes a background appearance model indicating the appearance of the background, and at least one among the one or more foreground model(s) includes a transition probability, with which a state corresponding to the position of the foreground on the background is transitioned by an action performed by the object corresponding to the foreground, for each action, and a foreground appearance model indicating the appearance of the foreground.

Abstract: An information input apparatus includes an observation unit that observes an environment including a user and one or more apparatuses to be controlled and includes a sensor; a learning unit that separates a foreground including the user and the one or more apparatuses to be controlled and a background including the environment except for the foreground from observation data obtained by the observation unit and learns three-dimensional models of the foreground and the background; a state estimation unit that estimates positions and postures of already modeled foregrounds in the environment; a user recognition unit that identifies fingers of the user from the foreground and recognizes a shape, position, and posture of the fingers; and an apparatus control unit that outputs a control command to the one or more apparatuses to be controlled on the basis of the recognized shape, position, and posture of the fingers.

Abstract: An HMM (Hidden Markov Model) learning device includes: a learning unit for learning a state transition probability as the function of actions that an agent can execute, with learning with HMM performed based on actions that the agent has executed, and time series information made up of an observation signal; and a storage unit for storing learning results by the learning unit as internal model data including a state-transition probability table and an observation probability table; with the learning unit calculating frequency variables used for estimation calculation of HMM state-transition and HMM observation probabilities; with the storage unit holding the frequency variables corresponding to each of state-transition probabilities and each of observation probabilities respectively, of the state-transition probability table; and with the learning unit using the frequency variables held by the storage unit to perform learning, and estimating the state-transition probability and the observation probability bas

Abstract: There is provided an information processing apparatus including a manipulation model learning unit configured to learn a manipulation model regarding manipulation of a first object by a second object, by use of an actual image that is an actually observed image including the first object and the second object, the manipulation model associating a position and a change in state of the second object, when a state of the second object changes at a position in an object reference coordinate system with the first object regarded as a reference, with a change in state of the first object caused by the change in state of the second object.

Abstract: An information input apparatus includes an observation unit that observes an environment including a user and one or more apparatuses to be controlled and includes a sensor; a learning unit that separates a foreground including the user and the one or more apparatuses to be controlled and a background including the environment except for the foreground from observation data obtained by the observation unit and learns three-dimensional models of the foreground and the background; a state estimation unit that estimates positions and postures of already modeled foregrounds in the environment; a user recognition unit that identifies fingers of the user from the foreground and recognizes a shape, position, and posture of the fingers; and an apparatus control unit that outputs a control command to the one or more apparatuses to be controlled on the basis of the recognized shape, position, and posture of the fingers.

Abstract: An information processing device includes: a foreground state estimating unit configured to estimate a foreground state of an image using an actual image which is an image to be actually observed; and a visible model updating unit configured to update a background visible model which is visibility of the background of an image and a foreground visible model which is visibility of the foreground using an estimation result of the foreground state.

Abstract: An information processing device includes: a learning section configured to learn a state transition probability model defined by state transition probability for each action of a state making a state transition due to an action performed by an agent capable of performing action and observation probability of a predetermined observed value being observed from the state, using an action performed by the agent and an observed value observed in the agent when the agent has performed the action.

Abstract: A learning device, learning method, and program for learning a pattern are disclosed. A learning device includes: a plurality of learning modules, each of which performs update learning to update a plurality of model parameters of a pattern learning model that learns a pattern using input data; model parameter sharing means for causing two or more learning modules from among the plurality of learning modules to share the model parameters; and sharing strength updating means for updating sharing strengths between the learning modules so as to minimize learning errors when the plurality of model parameters are updated by the update learning.

Abstract: A device for implementing a pattern learning model, the device including a plurality of learning modules, each of which performs update learning to update a plurality of model parameters of the pattern learning model that learns a pattern using input data. The device further including a model parameter sharing means for causing two or more learning modules from among the plurality of learning modules to share the model parameters; and a classification means for classifying the plurality of learning modules on the basis of the plurality of model parameters of each of the learning modules after the update learning.

Abstract: An information processing device includes a learning unit that performs, using an action performed by an object and an observation value of an image as learning data, learning of a separation learning model that includes a background model that is a model of the background of the image and one or more foreground model(s) that is a model of a foreground of the image, which can move on the background, in which the background model includes a background appearance model indicating the appearance of the background, and at least one among the one or more foreground model(s) includes a transition probability, with which a state corresponding to the position of the foreground on the background is transitioned by an action performed by the object corresponding to the foreground, for each action, and a foreground appearance model indicating the appearance of the foreground.

Abstract: A data processing apparatus includes: predicting means for calculating a prediction value of time series data with respect to input of the time series data using a prediction model for predicting the time series data; determining means for determining a target value of the time series data on the basis of the prediction value of the time series data; error calculating means for calculating an error of the prediction value relative to the target value; and retrieving means for retrieving error reduction data as input of the time series data to the prediction model for reducing the error of the prediction value.

Abstract: An information processing device includes an obtaining unit to obtain two pieces of information that are targets for searching for connections; a connection searching unit to use an action model wherein the manner of obtaining, from input information, obtain related information that relates to the input information is modeled, and find connection information to connect the two pieces of information, thereby searching connections between the two pieces of information; and a search result output unit to output the search results of the connections between the two pieces of information.

Abstract: An HMM (Hidden Markov Model) learning device includes: a learning unit for learning a state transition probability as the function of actions that an agent can execute, with learning with HMM performed based on actions that the agent has executed, and time series information made up of an observation signal; and a storage unit for storing learning results by the learning unit as internal model data including a state-transition probability table and an observation probability table; with the learning unit calculating frequency variables used for estimation calculation of HMM state-transition and HMM observation probabilities; with the storage unit holding the frequency variables corresponding to each of state-transition probabilities and each of observation probabilities respectively, of the state-transition probability table; and with the learning unit using the frequency variables held by the storage unit to perform learning, and estimating the state-transition probability and the observation probability bas

Abstract: An information processing device includes: a learning section configured to learn a state transition probability model defined by state transition probability for each action of a state making a state transition due to an action performed by an agent capable of performing action and observation probability of a predetermined observed value being observed from the state, using an action performed by the agent and an observed value observed in the agent when the agent has performed the action.

Abstract: An information processing device includes: a calculating unit configured to calculate a current-state series candidate that is a state series for an agent capable of actions reaching the current state, based on a state transition probability model obtained by performing learning of the state transition probability model stipulated by a state transition probability that a state will be transitioned according to each of actions performed by an agent capable of actions, and an observation probability that a predetermined observation value will be observed from the state, using an action performed by the agent, and an observation value observed at the agent when the agent performs an action; and a determining unit configured to determine an action to be performed next by the agent using the current-state series candidate in accordance with a predetermined strategy.

Abstract: An information processing apparatus including a learning unit that learns a predetermined time-series pattern. An output unit outputs a time-series pattern corresponding to the result of learning by the learning unit. An adjusting unit supplied with a time-series pattern obtained from an action by an action unit on the basis of a time-series pattern supplied from the output unit and external teaching for the action adjusts a time-series pattern supplied from the output unit correspondingly to the input time-series pattern. The learning unit learns the time-series pattern supplied from the output unit and adjusted by the adjusting unit.

Abstract: A data processing device for processing time-sequence data includes a learning unit for performing self-organizing learning of a SOM (self-organization map) making up a hierarchical SOM in which a plurality of SOMs are connected so as to construct a hierarchical structure, using, as SOM input data which is input to the SOM, a time-sequence of node information representing a winning node of a lower-order SOM which is at a lower hierarchical level from the SOM.