The principle of boosting in supervised learning involves combining multiple
weak classifiers to obtain a stronger classifier. AdaBoost has the reputation
to be a perfect example of this approach. We have previously shown that
AdaBoost is not truly an optimization algorithm. This paper shows that AdaBoost
is an algorithm in name only, as the resulting combination of weak classifiers
can be explicitly calculated using a truth table. This study is carried out by
considering a problem with two classes and is illustrated by the particular
case of three binary classifiers and presents results in comparison with those
from the implementation of AdaBoost algorithm of the Python library
scikit-learn.

AdaBoost 不是真正的优化算法，而是一种将多个弱分类器组合成强分类器的方法，它的结果可以通过真值表进行明确计算。本研究以两个类别的问题为例，以三个二元分类器为例，与 Python 库 scikit-learn 中的 AdaBoost 算法进行比较。

解读分析计算在 AdaBoost 编码中的失效

When Analytic Calculus Cracks AdaBoost Code

Positive-unlabeled (PU) learning deals with binary classification problems
when only positive (P) and unlabeled (U) data are available. Many recent PU
methods are based on neural networks, but little has been done to develop
boosting algorithms for PU learning, despite boosting algorithms' strong
performance on many fully supervised classification problems. In this paper, we
propose a novel boosting algorithm, AdaPU, for PU learning. Similarly to
AdaBoost, AdaPU aims to optimize an empirical exponential loss, but the loss is
based on the PU data, rather than on positive-negative (PN) data. As in
AdaBoost, we learn a weighted combination of weak classifiers by learning one
weak classifier and its weight at a time. However, AdaPU requires a very
different algorithm for learning the weak classifiers and determining their
weights. This is because AdaPU learns a weak classifier and its weight using a
weighted positive-negative (PN) dataset with some negative data weights $-$ the
dataset is derived from the original PU data, and the data weights are
determined by the current weighted classifier combination, but some data
weights are negative. Our experiments showed that AdaPU outperforms neural
networks on several benchmark PU datasets, including a large-scale challenging
cyber security dataset.

提出了一种新的正无标记（PU）学习算法，AdaPU 算法，通过在加权的正负（PN）数据集上进行学习单个弱分类器及其权重来优化经验指数损失，该算法在多个基准 PU 数据集，包括一个大型有挑战性的网络安全数据集上，优于神经网络。

一种正 - 无标记学习的提升算法

A Boosting Algorithm for Positive-Unlabeled Learning

Boosting combines weak classifiers to form highly accurate predictors.
Although the case of binary classification is well understood, in the
multiclass setting, the "correct" requirements on the weak classifier, or the
notion of the most efficient boosting algorithms are missing. In this paper, we
create a broad and general framework, within which we make precise and identify
the optimal requirements on the weak-classifier, as well as design the most
effective, in a certain sense, boosting algorithms that assume such
requirements.

本文提出了一种广泛且通用的框架，通过精确分析和确定弱分类器的最优需求，以及设计最有效的某种算法来应对多类别分类中存在的问题。