Self-paced learning (SPL) is a learning regime inspired by the learning process of humans and animals that gradually incorporates samples into training set from easy to complex by assigning a weight to each training sample. SPL incorporates a self-paced regularizer into the objective function to control the learning process. At present, there are various forms of SP regularizers and different regularizers may lead to distinct learning performance. Mixture weighting regularizer has the characteristics of both hard weighting and soft weighting. Therefore, it is widely used in many SPL-based applications. However, the current mixture weighting method only considers logarithmic soft weighting, which is relatively simple. In addition, in comparison with soft weighting or hard weighting, more parameters are introduced in the mixture weighting scheme. In this study, an adaptive mixture weighting SP regularizer is proposed to overcome the above issues. On the one hand, the representation form of weights can be adjusted adaptively during the learning process; on the other hand, the SP parameters introduced by mixture weighting can be adapted according to the characteristics of sample loss distribution, so as to be fully free of the empirically adjusted parameters. The experimental results on action recognition and multimedia event detection show that the proposed method is able to adjust the weighting form and parameters adaptively.