Please select the most similar patent number from A, B, C and D. Which number is?
Disclosed is a self-learning online update method for a multi-classification model, relating to artificial intelligence. The method comprises: according to a preset statistical period, performing monitoring and compiling statistics on the prediction performance of a model to be updated, and storing, in a statistical database, a predication performance statistical result in each statistical period (S110); checking data in the statistical database by using a preset trigger mechanism, so as to determine whether said model needs to be updated online (S120); if said model needs to be updated online, acquiring online newly generated data, and updating training data of said model according to the newly generated data (S130); and updating and training said model by using the updated training data, so as to obtain an updated multi-classification model (S140). The present application further relates to blockchain technology. The statistical database is stored in a blockchain. The existing problems of the prediction precision of a multi-classification model being significantly reduced as time goes by, and the multi-classification model being unable to be automatically updated can be solved.
A: A federated-learning-based user data classification method and apparatus, and a device and a medium, which relate to the technical field of information security. The method comprises: acquiring training data, training a local model by means of the training data, and generating a gradient matrix on the basis of a change gradient of a weight matrix of the local model during each training cycle; taking a preset upload rate as a channel selection threshold value, selecting, from a calculation result, a channel conforming to the channel selection threshold value to serve as a target channel, and taking a gradient matrix in the target channel as a target gradient matrix; selecting a target gradient matrix with a non-zero parameter as the weight of a server; updating the local model according to the weight of the server; and classifying user data according to an updated local model. The present invention also relates to blockchain technology. The training data is stored in a blockchain. By generating an updated local model, the security of private data of a user is protected during user data classification.
B: A training model updating method and system, and an agent, a server and a computer-readable storage medium, relating to the field of fintech. The method comprises the steps of: a federated learning server receiving a first training model sent by each reinforcement learning agent that runs in a real-time environment, and receiving a second training model that is sent by a data server and is obtained by training historical sample data (S10); performing federated learning on the first training model and the second training model to obtain a federation model (S20); and sending the federation model to the data server and each reinforcement learning agent for the data server and each reinforcement learning agent to correspondingly update the training models according to the federation model (S30).
C: The present application relates to the technical field of artificial intelligence, and discloses a classification model training method, a hyper-parameter searching method, and a device. The training method comprises obtaining a target hyper-parameter of a classification model to be trained, the target hyper-parameter being used for controlling a gradient update step length of said classification model, said classification model comprising a scale-invariant linear layer, the scale-invariant linear layer keeping a prediction classification result, outputted when a weight parameter of said classification model is multiplied by any scale coefficient, unchanged; and updating the weight parameter of said classification model according to the target hyper-parameter and a target training mode to obtain a trained classification model, the target training mode enabling the modulus length of the weight parameter of said classification model before update to be the same as that of the updated weight parameter of said classification model. On the basis of the technical solution of the present application, computation resources consumed by training the classification model can be reduced under the condition of ensuring the precision of the classification model.
D: A text classification model training method and apparatus, a text classification method and apparatus, a computer device and a medium, which relate to the fields of artificial intelligence and model construction. By means of uploading a model parameter of a local classification model and a local classification result to a federated learning server for performing joint learning, the security of text data in a model training process can be improved. The training method comprises: acquiring a text training set, and performing text classification training on a local classification model according to the text training set; encrypting a model parameter of the trained local classification model and a local classification result, and then uploading the encrypted model parameter and local classification result to a federated learning server for performing joint learning so as to obtain a learning parameter; and receiving the learning parameter sent by the federated learning server, updating the trained local classification model according to the learning parameter, and taking the updated local classification model as a trained text classification model. The present application further relates to blockchain technology. The text training set can be stored in a blockchain.
Answer: B