## 氏名: ガイベ アハマド アンマール (289934095)

## 論文題目: Ａ Generalization of Majority Vote Boosting Algorithm in the Agnostic
Learning Model

## 論文概要

In this research we have studied and developed a Boosting algorithm that can
work in the agnostic learning model. The agnostic learning model is a
generalization of the basic PAC-learning model. It aims to overcome the
shortcomings of the PAC-learning model such as the strong assumption of the
target concept.
We can model the learning process as consisting of two phases, a training
phase and a performance process. In the training phase the learner is
presented with labeled instances (called examples), drawn from the input
space according to an arbitrary but fixed distribution. Based on these
examples the learner must devise a hypothesis of the learned phenomenon
(usually called the target concept). In the performance phase the hypothesis
is used to classify instances from the input space, and the accuracy of the
hypothesis is evaluated. Under this scenario Boosting algorithms aim to
improve the accuracy of weak learning algorithms. A weak learning algorithm
is an efficient algorithm whose hypothesis is only slightly better than a
random guessing. By slightly better than random guessing we mean a
hypothesis that correctly classify an instance with probability just
exceeding 1/2 by a small value \gamma (0 < \gamma < 1/2).
As normal Boosting algorithms our algorithm works by running the weak
algorithm several times, each time on a different distribution of instances,
to generate several different hypotheses. Each of these hypotheses has an
accuracy very closed to the random guess. We will call these hypotheses weak
hypotheses. These weak hypotheses are combined by the boosting algorithm
into a single more complex and more accurate hypothesis. The different
distributions are generated using “filtering” process by which part of the
random examples that are presented to the boosting algorithm are discarded,
and only a subset of the examples are passed to the weak learning
algorithms. The aim of this process is to force the learner to concentrate
on the difficult to learn examples.

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提出時刻：2001/02/06 16:40:51