Based on the structural risk minimization principle, the linear SVM aiming at finding the linear decision plane with the maximal margin in the input space has gained increasing popularity due to its generalizability, efficiency and acceptable performance. However, rarely training data are evenly distributed in the input space , which leads to a high global VC confidence , downgrading the performance of the linear SVM classifier. Partitioning the input space in tandem with local learning may alleviate the unevenly data distribution problem. However, the extra model complexity introduced by partitioning frequently leads to overfitting. To solve this problem, we proposed a new supervised learning algorithm, Randomized Support Vector Forest (RSVF): Many partitions of the input space are constructed with partitioning regions amenable to the corresponding linear SVMs. The randomness of the partitions is injected through random feature selection and bagging. This partition randomness prevents the overfitting introduced by the over-complicated partitioning. We extensively evaluate the performance of the RSVF on several benchmark datasets, originated from various vision applications, including the four UCI datasets, the letter dataset, the KTH and the UCF sports dataset, and the Scene-15 dataset. The proposed RSVF outperforms linear SVM , kernel SVM, Random Forests (RF), and a local learning algorithm, SVM-KNN, on all of the evaluated datasets. The classification speed of the RSVF is comparable to linear SVM.