Transaction fraud detection is an essential topic in financial research, protecting customers
and financial institutions from suffering significant financial losses. The existing ensemble …

The classic algorithm AdaBoost allows to convert a weak learner, that is an algorithm that
produces a hypothesis which is slightly better than chance, into a strong learner, achieving …

Abstract Support Vector Machines (SVMs) are among the most fundamental tools for binary
classification. In its simplest formulation, an SVM produces a hyperplane separating two …

Partial multi-label learning deals with the circumstance in which the ground-truth labels are
not directly available but hidden in a candidate label set. Due to the presence of other …

It has been reported in many recent works on deep model compression that the population
risk of a compressed model can be even better than that of the original model. In this paper …

AdaBoost is a classic boosting algorithm for combining multiple inaccurate classifiers
produced by a weak learner, to produce a strong learner with arbitrarily high accuracy when …

Boosting is one of the most successful ideas in machine learning, achieving great practical
performance with little fine-tuning. The success of boosted classifiers is most often attributed …

Ensemble learning trains and combines multiple base learners for a single learning task,
and has been among the state-of-the-art learning techniques. Ensemble pruning tries to …

The aim of boosting is to convert a sequence of weak learners into a strong learner. At their
heart, these methods are fully sequential. In this paper, we investigate the possibility of …

Recent research has used margin theory to analyze the generalization performance for
deep neural networks (DNNs). The existed results are almost based on the spectrally …