KEYWORDS: Machine learning, Education and training, Matrices, Lawrencium, Data modeling, Cooccurrence matrices, Mining, Mathematical modeling, Information technology, Industry
Many multi-source transfer learning methods use the latent attributes between source domains to mitigate interference. However, these strategies have some shortcomings. Strategies that only use commonality may lose valuable latent information specific to a single source domain when extracting knowledge. Strategies that focus on specificity are limited by the differences between source domains, so they cannot effectively integrate different knowledge. For strategies that include both attributes, they only explore the knowledge scattered in the original source domains, and do not attempt to explore additional knowledge, which can fill the learning gap and promote knowledge fusion. Based on the above problems, this paper proposes a multi-source transfer learning method that can deeply mine the commonality and characteristics of multiple source domains and fully integrate all the latent information by reconstructing the multisource latent feature space and combining high-level concept learning. Finally, we conduct a large number of experiments to verify the effectiveness and superiority of the algorithm.
As the advancement of computer technique and artificial intelligence technology, education based on computer technology has become a hot topic, and the wave of educational informatization is rising. Among them, adaptive learning has received widespread attention as a type of educational informatization technology. However, the current adaptive learning feature models use different semantics to describe each other, and the models lack meta model level explanations, resulting in a lack of further operability of the models and difficulty in integrating with each other. In order to solve these problems, our paper proposes a fusion and verification method for learning feature adaptive models based on meta models. Above all, an adaptive learning feature meta model is proposed, which defines the construction rules of the adaptive learning feature model at meta model level, so that different models can be fused using the same set of semantic rules. Then, at the meta model level, transform the learning model into a requirement model. Finally, using formal methods, validate the fused feature model, which can make sure of the semantic consistency between the previous and subsequent models. This article demonstrates the entire process through a model fusion experiment. The experimental results indicate that this method is correct and feasible in promoting feature model fusion without adaptive learning.
KEYWORDS: Performance modeling, Interpolation, Data modeling, Semantics, Education and training, Matrices, Mathematical optimization, Machine learning, Systems modeling, Process modeling
In the field of source-based software defect prediction, it is necessary to convert the source code into the data form that the model can process, which is called word embedding. The commonly used word embedding models are Word2Vec and BERT models. Meanwhile, in software defect prediction, accurate feature representation is crucial to the performance of the defect prediction model. However, feature redundancy problems in source code, such as highly similar word vectors caused by code appearing in pairs, result in a set of word vectors generated when training word embedding models that may degrade model performance when applied to defect prediction tasks. In order to alleviate this problem, a CodeSift method is proposed in this paper. By calculating the similarity of each pair of word vectors in the code word vectors generated by the word embedding model and interpolating the generated code word vectors, the problem of feature redundancy between word vectors is reduced. CodeSift generates a new word vector from a highly similar word vector, and retains the original information by assigning weights, thus improving the compactness and information richness of feature representation. Experiments show that the F1 value of the defect prediction model is improved by using CodeSift method, and the false positive rate is lower than that of the original model.
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