4 results about "Structural risk minimization" patented technology

The invention discloses a sugarcane planting yield prediction and evaluation method and system based on edge calculation, and relates to the technical field of edge calculation. The method comprises the following steps: dividing a selected target planting area into N field units, and deploying an edge computing node on each field unit to obtain meteorological environment data; the technical key points are as follows: support vector regression is adopted as a local prediction model, optimization is performed in combination with insensitive loss and regularization terms, a prediction model with high generalization ability can be stably trained under a small sample condition so as to cope with selected planted crops, and by utilizing a structural risk minimization principle of SVR, a prediction model with high generalization ability can be obtained. Dynamic balance between fitting effectiveness and robustness is realized after corresponding processing in the target function; meanwhile, the SVR only reserves key historical samples as support vectors, real-time reasoning can be carried out, and then a personalized prediction system in which one field unit corresponds to one model is effectively constructed.

This application discloses a method, apparatus, electronic device, and program product for screening high-net-worth users, relating to the field of user profiling technology. The method includes: acquiring feature data of users to be screened, including user occupation information, terminal price information, luxury goods interaction information, car owner identity information, and average monthly consumption contribution; inputting the feature data into a user net worth screening model to obtain the net worth screening result for the users to be screened, the user net worth screening model being trained based on the XGBoost ensemble learning algorithm and through structural risk minimization; and determining whether the users to be screened are high-net-worth users based on the net worth screening result. This application, by using a user net worth screening model to determine whether a user belongs to high-net-worth users based on multi-dimensional feature data, effectively improves the accuracy of identifying high-net-worth users from communication users.

A domain self-adaptive method for solving subject difference in motor imagery brain-computer interface belongs to the technical field of transfer learning in motor imagery brain-computer interface. The method solves the problem of low classification accuracy of the MI-BCI system caused by the difference of the electroencephalogram signals between subjects. The method makes full use of the information of the source domain and the target domain samples, combines data processing and classification algorithms, and significantly improves the recognition accuracy and robustness of the motor imagery task. Through efficient data preprocessing and feature extraction method in domain self-adaptive manifold embedding, the consistency of the feature mapping of the training set and the target domain data is ensured. The classifier optimization based on the principle of structural risk minimization further enhances the classification performance. Through feature fusion and voting mechanism, the reliability of label classification is effectively improved. The method can effectively avoid the problem of low classification accuracy of the MI-BCI system caused by the difference of the electroencephalogram signals between subjects. The method can be applied to the electroencephalogram signal classification in motor imagery brain-computer interface.

The invention discloses an optimal kernel combination method for large-scale multi-label data stream classification in the technical field of machine learning, and the method comprises the steps: introducing a random Fourier feature RFF technology, and enabling a plurality of predefined kernel functions to be explicitly mapped into fixed-dimension feature vectors; linear increase of storage and calculation overhead along with data volume caused by kernel matrix implicit calculation is effectively avoided; the learning robustness is improved by adopting a fixed label threshold strategy; an improved online updating mechanism is provided, the problem of structural risk minimization of a multi-label kernel classifier and a combination coefficient thereof is directly solved, the combination coefficient is updated through index gradient descent, and an upper bound is constructed by using a Jensen inequality to realize independent and efficient online gradient descent updating of each single-kernel classifier, so that the classifiers and the combination coefficient are synchronously optimized, and the robustness of the multi-label kernel classifier is improved. Complementary advantages of different kernels are fully mined, multiple rounds of learning of the same sample are supported, and the method has higher prediction accuracy and expansibility and is suitable for large-scale data stream scenes such as text classification and image annotation.