Search results

By introducing Stacking algorithm to solve the underfitting problem caused by insufficient data in traditional machine learning, this paper provides a new solution to the cold start problem of entrepreneurial borrowing risk control.

The authors introduce semi-supervised learning and integrated learning into the field of migration learning, and innovatively propose the Stacking model migration learning, which can independently train models on entrepreneurial borrowing credit data, and then use the migration strategy itself as the learning object, and use the Stacking algorithm to combine the prediction results of the source domain model and the target domain model.

The effectiveness of the two migration learning models is evaluated with real data from an entrepreneurial borrowing. The algorithmic performance of the Stacking-based model migration learning is further improved compared to the benchmark model without migration learning techniques, with the model area under curve value rising to 0.8. Comparing the two migration learning models reveals that the model-based migration learning approach performs better. The reason for this is that the sample-based migration learning approach only eliminates the noisy samples that are relatively less similar to the entrepreneurial borrowing data. However, the calculation of similarity and the weighing of similarity are subjective, and there is no unified judgment standard and operation method, so there is no guarantee that the retained traditional credit samples have the same sample distribution and feature structure as the entrepreneurial borrowing data.

From a practical standpoint, on the one hand, it provides a new solution to the cold start problem of entrepreneurial borrowing risk control. The small number of labeled high-quality samples cannot support the learning and deployment of big data risk control models, which is the cold start problem of the entrepreneurial borrowing risk control system. By extending the training sample set with auxiliary domain data through suitable migration learning methods, the prediction performance of the model can be improved to a certain extent and more generalized laws can be learned.

This paper introduces the thought method of migration learning to the entrepreneurial borrowing scenario, provides a new solution to the cold start problem of the entrepreneurial borrowing risk control system and verifies the feasibility and effectiveness of the migration learning method applied in the risk control field through empirical data.

The purpose of this paper is to study the application ways of extended reality (XR) in teaching, the specific teaching effects and how to implement teaching integration.

The study adopts an umbrella review approach, locates and screens 20 pertinent meta-analysis studies published in international journals and conducts a systematic review of the effects of teaching applications of XR technologies in terms of subject categories, education levels and teaching cycles.

The study finds that the effects of virtual reality and augmented reality technologies on teaching effectiveness can reach up to 0.723 and 0.951, indicating that XR technologies can improve teaching.

Through the systematic analysis of 20 related element analysis studies published in international journals, the specific methods and effects of XR technology applied to teaching are finally obtained and put forward the new application trend of XR promoting teaching effect.

In the past decade, more and more research has also focused on the specific methods and effects of XR technology applied to teaching. However, there are still problems such as unclear technology application path and unclear effect. Based on this, it is necessary to further conduct an overall overview and analysis of the impact of XR technology on teaching effect when it is integrated into teaching, systematically study the teaching effect of XR technology in different education levels and subject types and provide a theoretical basis for more detailed research on XR technology integration into teaching.