Abstract: Machine learning tasks are usually single-task learning. However, in practical applications, the learning tasks are often related. As a result, the correlation between tasks is often ignored, and the complexity of samples is sometimes not considered in single-task learning. To address this, this paper proposes a new inductive transfer learning model based on the self-paced learning strategyby constructing a prediction model to learn the shared parameters of multiple related source tasks. First, the proposed model uses the strategy of self-paced learning to jointly learns the multiple related source tasks, and weights the learning samples according to the loss and difficulty degree of the samples in the source tasks. The parameters of the self-paced learning are iteratively updated to select the optimal samples with the less loss. Then, the knowledge learned from multiple related source tasks guide the learning of target tasks to construct multiple models for related transfer learning target tasks, and transfer these models to related target tasks to improve their generalization ability. Finally, we optimize the target model by using the Lagrange function to improve the performance of the classifiers. Experimental results show that the proposed model is superior to the existing transfer learning model under the same experimental conditions.

Key words: self-paced learning, inductive transfer learning, support vector machines