Optimal AdaBoost Converges
This work provides foundational theoretical validation for the AdaBoost algorithm, addressing a key problem in machine learning theory for researchers and practitioners.
The paper tackles the problem of proving the convergence properties of AdaBoost's classifier and margins, presenting formal proofs that confirm long-standing conjectures and show that these quantities converge to values consistent with decades of research.
The following work is a preprint collection of formal proofs regarding the convergence properties of the AdaBoost machine learning algorithm's classifier and margins. Various math and computer science papers have been written regarding conjectures and special cases of these convergence properties. Furthermore, the margins of AdaBoost feature prominently in the research surrounding the algorithm. At the zenith of this paper we present how AdaBoost's classifier and margins converge on a value that agrees with decades of research. After this, we show how various quantities associated with the combined classifier converge.