We present a new boosting algorithm, motivated by the large margins theory for boosting. We give experimental evidence that the new algorithm is significantly more robust against label noise than existing boosting algorithm.

Despite the limitations imposed by the proportional hazards assumption, the Cox model is probably the most popular statistical tool used to analyze survival data, thanks to its flexibility and ease of interpretation. For this reason, novel statistical/machine learning techniques are usually adapted to fit it, including boosting, an iterative technique originally developed in the machine learnin...

Risk and, thus, the volatility of financial asset prices plays a major role in financial decision making and financial regulation. Therefore, understanding and predicting the volatility of financial instruments, asset classes or financial markets in general is of utmost importance for individual and institutional investors as well as for central bankers and financial regulators. In this paper w...

This paper presents a novel way to speed up the classification time of a boosting classifier. We make the shallow (flat) network deep (hierarchical) by growing a tree from the decision regions of a given boosting classifier. This provides many short paths for speeding up and preserves the reasonably smooth decision regions of the boosting classifier for good generalisation. We express the conve...

The implementation of tree-ensemble models has become increasingly essential in solving classification and prediction problems. Boosting ensemble techniques have been widely used as individual machine learning algorithms predicting house prices. One the is LGBM algorithm that employs leaf wise growth strategy, reduces loss improves accuracy during training which results overfitting. However, XG...

In this paper we study boosting methods from a new perspective. We build on recent work by Efron et al. to show that boosting approximately (and in some cases exactly) minimizes its loss criterion with an l1 constraint on the coefficient vector. This helps understand the success of boosting with early stopping as regularized fitting of the loss criterion. For the two most commonly used criteria...

We study the task of online boosting — combining online weak learners into an online strong learner. While batch boosting has a sound theoretical foundation, online boosting deserves more study from the theoretical perspective. In this paper, we carefully compare the differences between online and batch boosting, and propose a novel and reasonable assumption for the online weak learner. Based o...