The article discusses the behavior of ADABOOST in different noise regimes and proposes modifications to achieve *soft margins* to improve noise robustness. ADABOOST is shown to achieve a *hard margin* in the low noise regime, where it concentrates on a few hard-to-learn patterns, similar to support vectors. However, in high noise regimes, this hard margin leads to overfitting. The authors propose regularized versions of ADABOOST, such as ADABOOSTREG and LP/QP-ADABOOST, which introduce a *soft margin* by allowing for misclassifications. These algorithms aim to balance the trade-off between trusting the data and adjusting for noisy patterns. The analysis shows that ADABOOST can be viewed as a gradient descent method in the margin space, and that the hard margin is suboptimal in the presence of noise. The proposed soft margin approaches, including regularization and slack variables, are shown to yield competitive results on noisy data. The paper also highlights the connection between ADABOOST and support vector machines (SVMs), and demonstrates that the margin distributions of ATAs resemble those of SVMs. The results show that ADABOOST-type algorithms can achieve a hard margin asymptotically, but with noise, a soft margin is more effective. The article concludes with experimental results demonstrating the effectiveness of the proposed regularized ADABOOST algorithms in handling noisy data.The article discusses the behavior of ADABOOST in different noise regimes and proposes modifications to achieve *soft margins* to improve noise robustness. ADABOOST is shown to achieve a *hard margin* in the low noise regime, where it concentrates on a few hard-to-learn patterns, similar to support vectors. However, in high noise regimes, this hard margin leads to overfitting. The authors propose regularized versions of ADABOOST, such as ADABOOSTREG and LP/QP-ADABOOST, which introduce a *soft margin* by allowing for misclassifications. These algorithms aim to balance the trade-off between trusting the data and adjusting for noisy patterns. The analysis shows that ADABOOST can be viewed as a gradient descent method in the margin space, and that the hard margin is suboptimal in the presence of noise. The proposed soft margin approaches, including regularization and slack variables, are shown to yield competitive results on noisy data. The paper also highlights the connection between ADABOOST and support vector machines (SVMs), and demonstrates that the margin distributions of ATAs resemble those of SVMs. The results show that ADABOOST-type algorithms can achieve a hard margin asymptotically, but with noise, a soft margin is more effective. The article concludes with experimental results demonstrating the effectiveness of the proposed regularized ADABOOST algorithms in handling noisy data.