By analyzing the problem of job performance interference in distributed machine learning， it is found that performance interference is caused by the uneven allocation of GPU resources such as memory overload and bandwidth competition， and to this end， a mechanism for quickly predicting performance interference between jobs was designed and implemented， which can adaptively predict the degree of job interference according to the given GPU parameters and job types. First， the GPU parameters and interference rates during the operation of distributed machine learning jobs were obtained through experiments， and the influences of various parameters on performance interference were analyzed. Second， some GPU parameter-interference rate models were established by using multiple prediction technologies to analyze the job interference rate errors. Finally， an adaptive job interference rate prediction algorithm was proposed to automatically select the prediction model with the smallest error for a given equipment environment and job set to predict the job interference rates quickly and accurately. By selecting five commonly used neural network tasks， experiments were designed on two GPU devices and the results were analyzed. The results show that the proposed Adaptive Interference Prediction （AIP） mechanism can quickly complete the selection of prediction model and the performance interference prediction without providing any pre-assumed information， it has comsumption time less than 300 s and achieves prediction error rate in the range of 2% to 13%， which can be applied to scenarios such as job scheduling and load balancing.

It is called labels matching problem when two labels of an instance come from two labelsets respectively in multi-label classification, however there is no any specific algorithm for solving such problem. Although the labels matching problem could be solved by tranditional multi-label classification algorithms, but this problem has its own particularity. After analyzing the labels matching problem, a new labels matching algorithm based on pairwise labelsets was proposed using adaptive method, which considered the real Adaptive Boosting (real AdaBoost) and the global optimization idea. This algorithm could learn the rule of labels matching well and complete matching. The experimental results show that, compared with the traditional algorithms, the new algorithm can not only reduce searching scope of the labels space, but also decrease the minimum learning error as the number of weak classifiers increases, and make the classification more accurate and faster.