1263 results about "Learning unit" patented technology

The present invention provides a network security apparatus for monitoring telecommunication information flowing through a network and carrying out detection of, and / or protection from, an illegitimate intrusion to the network, comprising: an anomaly judgment unit for judging a presence or absence of an abnormality of the telecommunication information based on judgment reference information by using a statistical method; a learning unit for creating the judgment reference information from the telecommunication information; a first port for importing first telecommunication information currently from the network; a second port for importing second telecommunication information accumulated by a telecommunication information accumulation apparatus historically; and a telecommunication information allocation unit for allocating the first and second telecommunication information taken in respectively from the first and second ports to the anomaly judgment unit and the learning unit, wherein the learning unit creates the judgment reference information by learning the first and / or second telecommunication information.

A behavior control system of a robot for learning a phoneme sequence includes a sound inputting device inputting a phoneme sequence, a sound signal learning unit operable to convert the phoneme sequence into a sound synthesis parameter and to learn or evaluate a relationship between a sound synthesis parameter of a phoneme sequence that is generated by the robot and a sound synthesis parameter used for sound imitation, and a sound synthesizer operable to generate a phoneme sequence based on the sound synthesis parameter obtained by the sound signal learning unit.

A machine learning device that learns an operation of a robot for picking up, by a hand unit, any of a plurality of workpieces placed in a random fashion, including a bulk-loaded state, includes a state variable observation unit that observes a state variable representing a state of the robot, including data output from a three-dimensional measuring device that obtains a three-dimensional map for each workpiece, an operation result obtaining unit that obtains a result of a picking operation of the robot for picking up the workpiece by the hand unit, and a learning unit that learns a manipulated variable including command data for commanding the robot to perform the picking operation of the workpiece, in association with the state variable of the robot and the result of the picking operation, upon receiving output from the state variable observation unit and output from the operation result obtaining unit.

Certain embodiments of the present invention provide methods and systems for selecting an application for processing an electronic image file based upon the content of the image file. Certain embodiments provide an image selector selecting an image from an image storage device, an identification module providing a label to the image based on the content of the image, an application selector choosing at least one application from a plurality of applications to process the image, and a learning unit maintaining a record of image labels and the applications presently and previously processing the images.

The invention relates to a road traffic state detecting device based on omnibearing computer vision, comprising shooting devices and a microprocessor, wherein the shooting devices are installed on all measuring points in all roads in a road network; the microprocessor is used for evaluating the road traffic state according to the video data of the shooting devices and comprises a full-view image obtaining unit, a road service capability customizing unit, a road service level self-learning unit, a road service level judging unit, a detecting data processing unit and an artificial checking unit. The road traffic state detecting device based on omnibearing computer vision has wide detecting range, high detecting precision, good detecting instantaneity, convenient application, visual and clear detecting result, subjective sensitivity index data and objective evaluation index data and is convenient for all grades of road networks of a city to carry out comprehensive evaluation on the road traffic state in time and space.

A computer system that may, for example, be used for educational purposes delivers content, e.g., instructional content, to a plurality of users, e.g., students. The content is presented to such users in units, e.g., learning units. Each unit has associated with it an assessment information relating to each one of the users. One or more systems, e.g., learning systems, present the one or more units to one or more users in a first interactive environment, e.g., a first learning environment, and a second interactive environment, e.g., a second learning environment, that is different from the first interactive environment. A digital rights and asset management application controls access to the content associated with each one of said one or more units according to corresponding unit identifiers, e.g., learning unit identifiers, and presents such content to the plurality of users in the first and second interactive environments for assessment purposes. An assessment application, e.g., a grade book application, stores assessment information derived from presenting the content to said one or more users in the first and second interactive environments, with the unit identifier correlating the assessment information with the units.

Provided in the EoE technique are the node, the network, the correspondence relationship generating method and the frame transfer program to avoid traffic concentration on a specific link to improve throughput of the network as a whole by realizing optimum path transfer. The frame switching unit includes the frame analysis unit for analyzing an input frame kind and the like, the table search unit for obtaining frame rewriting information and output port information, the forwarding table storage unit for managing an output port of a frame, the MAC learning unit for executing MAC address learning, the EoE-MAC learning unit for learning a relationship between a MAC address and an EoE-MAC address, the STP control unit for executing processing of a spanning tree, and the like.

A system and method for developing a course requires a plurality of participants that collaborate over a network, for example, a social network. A database stores an initial framework that defines a sequence of learning units for creating a desired learning environment for students, those learning units made of specifically ordered content objects. The learning units are identified by corresponding learning unit identifiers. A plurality of workstations coupled to the network are used for entry of reviewer information by the participants using the learning environment created for the students. The reviewer information comprise one or more comments entered by one participant about a learning unit or content object and a rank entered by anther participant about the comment, with the rank being correlated with a defined ranking standard. A processor processes the rank according to a predefined criteria to produce a ranking result that is associated with a learning unit identifier. The ranking result is used for associating learning content to the learning unit identified by the learning unit identifier.

A learning apparatus performs a learning process for a feed-forward multilayer neural network with supervised learning. The network includes an input layer, an output layer, and at least one hidden layer having at least one probing neuron that does not transfer an output to an uppermost layer side of the network. The learning apparatus includes a learning unit and a layer quantity adjusting unit. The learning unit performs a learning process by calculation of a cost derived by a cost function defined in the multilayer neural network using a training data set for supervised learning. The layer quantity adjusting unit removes at least one uppermost layer from the network based on the cost derived by the output from the probing neuron, and sets, as the output layer, the probing neuron in the uppermost layer of the remaining layers.

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.

A feature information collecting device includes: a feature image recognizing unit that performs image recognition processing of a feature included in image information in a surrounding area of a vehicle; a construction information obtaining unit that obtains construction information including information of a construction section; a construction information storage unit that stores the obtained construction information; a construction completion determining unit that determines, when the vehicle travels a section of a road corresponding to a construction section according to the already stored construction information, completion of construction indicated by the construction information; and a feature learning unit that causes the feature image recognizing unit to perform image recognition processing of a feature in a construction section according to the construction information when the completion of construction is determined, and that generates, based on an image recognition result thereof and the vehicle position information, learned feature information including position information and attribute information of an image-recognized feature.

A face recognition apparatus and method using Principal Component Analysis (PCA) learning per subgroup, the face recognition apparatus includes: a learning unit which performs Principal Component Analysis (PCA) learning on each of a plurality of subgroups constituting a training data set, and then performs Linear Discriminant Analysis (LDA) learning on the training data set, thereby generating a PCA-based LDA (PCLDA) basis vector set of each subgroup; a feature vector extraction unit which projects a PCLDA basis vector set of each subgroup to an input image and extracts a feature vector set of the input image with respect to each subgroup; a feature vector storing unit which projects a PCLDA basis vector set of each subgroup to each of a plurality of face images to be registered, thereby generating a feature vector set of each registered image with respect to each subgroup, and storing the feature vector set in a database; and a similarity calculation unit which calculates a similarity between the input image and each registered image.

An action recognition apparatus includes an input unit for inputting image data, a moving-object detection unit for detecting a moving object from the image data, a moving-object identification unit for identifying the detected moving object based on the image data, a state detection unit for detecting a state or an action of the moving object from the image data, and a learning unit for learning the detected state or action by associating the detected state or action with meaning information specific to the identified moving object.

An information processing apparatus includes a first learning unit adapted to learn a first SOM (self-organization map), based on a first parameter extracted from an observed value, a winner node determination unit adapted to determine a winner node on the first SOM, a searching unit adapted to search for a generation node on a second SOM having highest connection strength with the winner node, a parameter generation unit adapted to generate a second parameter from the generation node, a modification unit adapted to modify the second parameter generated from the generation node, a first connection weight modification unit adapted to modify the connection weight when end condition is satisfied, a second connection weight modification unit adapted to modify the connection weight depending on evaluation made by a user, and a second learning unit adapted to learn the second SOM based on the second parameter obtained when the end condition is satisfied.

The present invention provides a communication device which can perform a routing process by selecting the optimum network without preparing a statically generated connection profile. A communication detection unit detects the start of communication by a communication unit, and activates a network selection unit. The network selection unit checks an application characteristic database on the basis of a parameter from the communication detection unit, and refers to a corresponding application characteristic. The network selection unit refers to a network characteristic database, and selects the optimum network. The communication unit connects the optimum network on the basis of the selection result. When the communication application starts communication, the communication detection unit notifies the communication contents learning unit of the communication contents. The communication contents learning unit analyzes the communication contents and acquires statistics, and updates the information about the application characteristic database.

The computer interactive system of the present invention recognizes a question from a user and outputs an answer by a synthesized voice. The system includes; a selection unit for selecting the answer from a database; an evaluation unit for evaluating a dialog between the user and computer and determining whether of not to continue the dialog. The evaluation unit may evaluate a dialog consistency, or dialog circumstance. Further, the selection unit selects and combines on the basis of the determination result the answer with one of a plurality of dialog sentences. The system may further comprises a learning unit for storing a novel answer unknown to the computer, by interrogating by using synthesize unit the user about the novel answer and further storing the interrogation scenario.

Provided is a system that generates models for classifying input data into a plurality of classes on the basis of training data previously classified into the plurality of classes. The system includes a sampling unit and a learning unit. The sampling unit samples, from the training data, a plurality of datasets each including a predetermined number of elements classified into a minority class and a corresponding number of elements classified into a majority class, the corresponding number being determined in accordance with the predetermined number. The learning unit learns each of a plurality of models for classifying the input data into the plurality of classes, by using a machine learning technique on the basis of each of the plurality of sampled datasets.

A system manages education, career planning, workforce mobility for students and workers includes a database configured to store a code corresponding to a classification of a course. The course can include a single learning unit or any combination of learning units. The database also stores learner information including at least one code corresponding to the classified course and one code corresponding to assessment information for the classified course. An interface is configured to suggest recommended courses based on the learner information.

A face recognition system based on adaptive learning includes a specific person detection and tracking unit for detecting and tracking a specific person from a moving image. A facial feature extraction unit extracts a plurality of facial feature vectors from the detected and tracked specific person. A face recognition unit searches for a given registration model by comparing the extracted facial feature vectors with facial feature vectors of the registration models previously stored in a user registration model database. A learning target selection unit selects a facial feature vector to be added to a record of the given registration model from among the extracted facial feature vectors. A registration model learning unit adds and updates the selected facial feature vector to the record of the given registration model.

The invention provides a machine learning device, a robot system, and a machine learning method. The machine learning device that learns an operation of a robot (14) for picking up, by a hand unit, any of a plurality of workpieces (12) placed in a random fashion, including a bulk-loaded state, includes a state variable observation unit (21) that observes a state variable representing a state of the robot, including data output from a three-dimensional measuring device (15) that obtains a three-dimensional map for each workpiece, an operation result obtaining unit (26) that obtains a result of a picking operation of the robot for picking up the workpiece by the hand unit, and a learning unit (22) that learns a manipulated variable including command data for commanding the robot to perform the picking operation of the workpiece, in association with the state variable of the robot and the result of the picking operation, upon receiving output from the state variable observation unit and output from the operation result obtaining unit.

A speaker identification device includes: a primary speaker identification unit that computes, for each pre-stored registered speaker, a score that indicates the similarity between input speech and speech of the registered speakers; a similar speaker selection unit that selects a plurality of the registered speakers as similar speakers according to the height of the scores thereof; a learning unit that creates a classifier for each similar speaker by sorting the speech of a certain similar speaker among the similar speakers as a positive instance and the speech of the other similar speakers as negative instances; and a secondary speaker identification unit that computes, for each classifier, a score of the classifier with respect to the input speech, and outputs an identification result.

An information processing device includes a feature amount extracting unit configured to extract the feature amount of each frame of an image of a content for detector learning of interest that is a content to be used for learning of a highlight detector which is a model for detecting a scene in which the user is interested as a highlight scene; a clustering unit configured to use cluster information that is the information of the cluster obtained by performing cluster learning; a highlight label generating unit configured to generate a highlight label sequence; and a highlight detector learning unit configured to perform learning of the highlight detector.

A vehicular control device includes (i) situation detection units that detect situation of the vehicle, occupants, or a surrounding area; (ii) a storage unit for storing a model representing association between variables indicating the situation and variables indicating presence or absence of operation needs for each in-vehicle unit, (iii) an inference execution unit that determines presence or absence of the operation needs for each in-vehicle unit using the stored model; (iv) a control unit that controls each in-vehicle unit based on a result of determination by the inference execution unit; (v) an operation detection unit that detects the operation of each in-vehicle unit; and (vi) a model learning unit that learns the stored model using the detected situation when the operation of each in-vehicle unit is detected by the operation detection unit, and an event involving the presence of operation needs for the in-vehicle unit.

A machine learning method and a machine learning device, and a fault prediction device and a fault prediction system. fault prediction system includes a machine learning device that learns conditions associated with a fault of an industrial machine. The machine learning device includes a state observation unit that, while the industrial machine is in operation or at rest, observes a state variable including, e.g., data output from a sensor, internal data of control software, or computational data obtained based on these data, a determination data obtaining unit that obtains determination data used to determine whether a fault has occurred in the industrial machine or the degree of fault, and a learning unit that learns the conditions associated with the fault of the industrial machine in accordance with a training data set generated based on a combination of the state variable and the determination data.

Provided are an apparatus and method for recognizing voice commands, the apparatus including: a voice command recognition unit which recognizes an input voice command; a voice command recognition learning unit which learns a recognition-targeted voice command; and a controller which controls the voice command recognition unit to recognize the recognition-targeted voice command from an input voice command, controls the voice command recognition learning unit to learn the input voice command if the voice command recognition is unsuccessful, and performs a particular operation corresponding to the recognized voice command if the voice command recognition is successful.

A machine learning device which learns fault prediction of one of a main shaft of a machine tool and a motor driving the main shaft, including a state observation unit observing a state variable including at least one of data output from a motor controller controlling the motor, data output from a detector detecting a state of the motor, and data output from a measuring device measuring a state of the one of the main shaft and the motor; a determination data obtaining unit obtaining determination data upon determining one of whether a fault has occurred in the one of the main shaft and the motor and a degree of fault; and a learning unit learning the fault prediction of the one of the main shaft and the motor in accordance with a data set generated based on a combination of the state variable and the determination data.

The invention discloses an unmanned vehicle control system based on deep learning. The unmanned vehicle control system comprises a controller, a learning unit and a learning module, the controller comprises a collection unit and an update unit, the collection unit is used for collecting control action information of a driver in a manual driving mode, environment variable information of a vehicle and preset route traveling precollimation point and vehicle body direction included angle, the learning module is used for optimizing a vehicle control algorithm according to collected data by means of deep learning, and the update unit is used for loading the optimized control algorithm acquired by the deep learning into an embedded processor of the controller.

The present invention aims to effectively restrict access of a general user to an inappropriate information piece at the judgment of an information manager. In order to pick up a desired information from an information storage medium or an information communication network while excluding inappropriate information, an information manager inputs suitability of information (a teacher signal) to an information presented at a user terminal 21 to a group profile learning unit 16 and prepares a group profile reflecting suitability of the information as specified by the information manager using the teacher signal. At a score calculating unit 13, a score signal indicating suitability of information is calculated for each information using the group profile, and access of an information user in a group to inappropriate information is restricted.