ABSTRACT

As applications of neural networks increase in our daily lives, their practicality and accuracy become more of a challenge as they are applied to approximate more complicated functions typically composed of different dependent or independent views. While the complexity of the functions and the number of views to be approximated or simulated increases, the task becomes more complicated and more difficult in that it may eventually jeopardise the classifier’s accuracy and make the results unreliable. This paper surveys an improved active learning method called Enhanced Multi-Learner (EML) to facilitate the approximation or simulation of complex functions via neural networks by distributing the complexities of the task under simulation among an array of learners where each network is responsible for learning a specific view. We experimented with EML realisations through neural networks to solve complex problems where traditional methods did not provide adequate results. These experimental studies were conducted in three different domains and are summarised here. Legacy solutions were also provided in each experiment, and the results were compared. The experimental results indicate the superiority of EML base neural networks in dealing with sophisticated pattern recognition problems.

摘要

随着神经网络在我们日常生活中的应用越来越多，它们的实用性和准确性变得更具挑战性，因为它们被应用于逼近通常由不同的依赖或独立视图组成的更复杂的函数。随着函数的复杂性和要近似或模拟的视图数量的增加，任务变得更加复杂和困难，因为它最终可能危及分类器的准确性并使结果不可靠。本文调查了一种改进的主动学习方法，称为增强型多学习器 (EML)，该方法通过将模拟任务的复杂性分布在一组学习器中，从而促进通过神经网络逼近或模拟复杂函数，其中每个网络负责学习具体观点。我们通过神经网络对 EML 实现进行了实验，以解决传统方法无法提供足够结果的复杂问题。这些实验研究在三个不同的领域进行，总结如下。每个实验中还提供了遗留解决方案，并对结果进行了比较。实验结果表明 EML 基础神经网络在处理复杂模式识别问题方面的优越性。