Local Rademacher Complexity Machine

Support Vector Machines (SVMs) are a state-of-the-art and powerful learning algorithm that can effectively solve many real world problems. SVMs are the transposition of the Vapnik–Chervonenkis (VC) theory into a learning algorithm. In this paper, we present the Local Rademacher Complexity Machine (LRCM), a transposition of the Local Rademacher Complexity (LRC) theory, the state-of-the-art evolution of the VC theory, into a learning algorithm. Analogously to what has been done for the SVMs, we will present first the theoretical ideas behind the LRC theory, we will show how these ideas can be translated into a learning algorithm, the LRCM, and then how the LRCM can be made efficient and kernelizable. By exploiting a series of real world datasets, we will show the effectiveness of the LRCM against the SVMs.