论文标题
自适应，动态，综合统计和信息理论学习
Self-Adaptive, Dynamic, Integrated Statistical and Information Theory Learning
论文作者
论文摘要
该论文分析并通过在神经网络培训中采用的各种错误度量的定位进行分析，并确定没有最好的措施，尽管在不同的学习情况下有一系列措施随着优势的变化。席尔瓦（Silva）及其研究合作伙伴出版的一种出色的，出色的措施，称为$ e_ {exp} $，代表了一个研究方向，可以将更多的措施与学习过程中的固定重要性相结合。该论文的主要思想是远远超越并将这种相对重要性整合到通过称为$ e_ {expabs} $的新型错误度量实现的神经网络训练算法中。这种方法包含在Levenberg-Marquardt培训算法中，因此，还引入了一种新颖的版本，从而导致了一种自适应，动态的学习算法。这种活力仅对所得模型的准确性有积极影响，而对训练过程本身也没有积极影响。所描述的综合算法测试证明，所提出的新算法动态整合了本文的关键新颖性统计和信息理论的两个重要世界。
The paper analyses and serves with a positioning of various error measures applied in neural network training and identifies that there is no best of measure, although there is a set of measures with changing superiorities in different learning situations. An outstanding, remarkable measure called $E_{Exp}$ published by Silva and his research partners represents a research direction to combine more measures successfully with fixed importance weighting during learning. The main idea of the paper is to go far beyond and to integrate this relative importance into the neural network training algorithm(s) realized through a novel error measure called $E_{ExpAbs}$. This approach is included into the Levenberg-Marquardt training algorithm, so, a novel version of it is also introduced, resulting a self-adaptive, dynamic learning algorithm. This dynamism does not has positive effects on the resulted model accuracy only, but also on the training process itself. The described comprehensive algorithm tests proved that the proposed, novel algorithm integrates dynamically the two big worlds of statistics and information theory that is the key novelty of the paper.
