Optimal AdaBoost Converges

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

Limit Cycles of AdaBoost

The iterative weight update for the AdaBoost machine learning algorithm may be realized as a dynamical map on a probability simplex. When learning a low-dimensional data set this algorithm has a tendency towards cycling behavior, which is the topic of this paper. AdaBoost's cycling behavior lends itself to direct computational methods that are ineffective in the general, non-cycling case of the algorithm. From these computational properties we give a concrete correspondence between AdaBoost's cycling behavior and continued fractions dynamics. Then we explore the results of this correspondence to expound on how the algorithm comes to be in this periodic state at all. What we intend for this work is to be a novel and self-contained explanation for the cycling dynamics of this machine learning algorithm

Environmental Estrogens and Breast Cancer Risk Bibliography

Bibliography on environmental estrogensBibliography of references on environmental estrogens.New York State Department of Health and Department of Environmental Conservatio

Endocrine Disruption and Breast Cancer Risk Bibliography

Bibliography on endocrine disruption and breast cancer riskBibliography on endocrine disruption and breast cancer risk.New York State Department of Health and Department of Environmental Conservatio