Abstract:
A picture ranking method and a terminal comprises acquiring pictures stored in a terminal, detecting whether the pictures are first-type pictures, where a first-type picture refers to a picture including a human face, and when the pictures are first-type pictures, ranking the pictures according to a social relation model, or when the pictures are not first-type pictures, ranking the pictures according to a preset rule.
Abstract:
A user behavior recognition method, a user equipment, a behavior recognition server, and a behavior recognition system are presented, where the method includes acquiring, by a first user equipment, statistical distribution information of a target parameter corresponding to a target user behavior, where the target parameter includes at least one parameter in a behavior recognition model of the target user behavior, and the statistical distribution information of the target parameter is determined according to values of the target parameters in behavior recognition models of the target user behavior that are respectively corresponding to multiple other user equipment; and creating and saving, according to the statistical distribution information, a behavior recognition model of the target user behavior, to recognize the target user behavior.
Abstract:
A method and a system for processing lifelong learning of a terminal, and an apparatus is presented. The method for processing lifelong learning of a terminal according to the present disclosure includes sending, to a server, a request for downloading a function module, where the download request includes description information of the function module; receiving the function module that is sent by the server and is corresponding to the description information; and using the function module to expand and/or update a local function. According to the embodiments of the present disclosure, the lifelong learning of the terminal is implemented, and a problem in the prior art that the terminal cannot perform function expansion and updating is resolved.
Abstract:
A method and an apparatus for establishing and using a user recommendation model in a social network. The method includes obtaining training data from the social network, performing heterogeneous data transfer learning on the training data to learn a semanteme of the training data, establishing an association between a user and the image data by using the text data as a medium, establishing a semantic association relationship between the image data and the user according to the semanteme of the training data and the association between the user and the image data, and establishing a user recommendation model according to the semantic association relationship, where the user recommendation model includes the semantic association relationship between the image data and the user.
Abstract:
A user behavior recognition method, a user equipment, a behavior recognition server, and a behavior recognition system are presented, where the method includes acquiring, by a first user equipment, statistical distribution information of a target parameter corresponding to a target user behavior, where the target parameter includes at least one parameter in a behavior recognition model of the target user behavior, and the statistical distribution information of the target parameter is determined according to values of the target parameters in behavior recognition models of the target user behavior that are respectively corresponding to multiple other user equipment; and creating and saving, according to the statistical distribution information, a behavior recognition model of the target user behavior, to recognize the target user behavior.
Abstract:
Embodiments of the present disclosure disclose a picture ranking method and a terminal. The picture ranking method comprises acquiring pictures stored in a terminal, detecting whether the pictures are first-type pictures, where the first-type picture refers to a picture including a human face, and when the pictures are first-type pictures, ranking the pictures according to a social relation model, or when the pictures are not first-type pictures, ranking the pictures according to a preset rule.
Abstract:
A method for processing information by an intelligent agent and the intelligent agent, where the method comprises: a first intelligent agent sends a request message to a second intelligent agent, where the request message includes an invitation message or a recommendation message; the first intelligent agent receives a decision message fed back by the second intelligent agent, where the decision message is determined according to the invitation message or the recommendation message and a knowledge model of the second intelligent agent; and the first intelligent agent updates, according to the decision message, a knowledge model of the first intelligent agent or sends a notification message to a first user account corresponding to the first intelligent agent. By using these technical solutions, information on a social network may be learned and processed by means of interaction with another intelligent agent, thereby implementing mining of data on the social network.
Abstract:
A method for recognizing a target object in an image, and an apparatus, where the method includes extracting feature data from an image, and transforming the extracted feature data into a uniform expression, performing automatic clustering for features in the image according to the feature data in the uniform expression and a historical clustering result, grouping a target object of a known class included in an automatic clustering result into the corresponding known class, in order to recognize a target object of the known class in the image, and training a classifier in a machine learning manner, for a target object of an unknown class included in the automatic clustering result, in order to recognize a target object of an unknown class in the image. In the embodiments of the present invention, recognizing a target object of an unknown class can be implemented.
Abstract:
A picture ranking method and a terminal comprises acquiring pictures stored in a terminal, detecting whether the pictures are first-type pictures, where a first-type picture refers to a picture including a human face, and when the pictures are first-type pictures, ranking the pictures according to a social relation model, or when the pictures are not first-type pictures, ranking the pictures according to a preset rule.
Abstract:
A picture ranking method and a terminal comprises acquiring pictures stored in a terminal, detecting whether the pictures are first-type pictures, where a first-type picture refers to a picture including a human face, and when the pictures are first-type pictures, ranking the pictures according to a social relation model, or when the pictures are not first-type pictures, ranking the pictures according to a preset rule.