DBN是一种堆叠许多独立的无监督网络的技术,这种网络使用每个网络的隐藏层作为下一层的输入。通常,使用受限玻尔兹曼机(RBM)或自动编码器的“堆栈” 。

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深度学习对设计模式组织自动化的影响:Implications of deep learning for the automation of design patterns organization

论文简介

虽然像其它领域,如电子邮件过滤,网页分类,情感分析,和作者识别,研究人员已经使用文本分类方法自动化组织和选择设计模式。然而,有必要在设计模式(即文档)与用于组织设计模式的特征之间的语义关系之间架起桥梁。在本研究中,我们提出了一种利用强大的深度学习算法深度信念网络 (DBN),以特征向量的形式去学习文档的语义表示方法。我们在一个基于文本分类的自动化系统中进行了一个案例研究,该系统用于软件设计模式的分类和选择。在案例研究中,我们重点研究了两个主要的研究目标:1)验证了除了所提出的方法之外,通过基于全局滤波器的特征选择方法构建的特征集的效果,2)利用该方法评估分类器分类决策(即模式组织)的改进效果。DBN参数的调整,例如一些隐藏层、节点和迭代,可以帮助开发人员构建更具说明性的特征集。实验结果表明,该方法对于构造更具代表性的特征集,提高分类器在设计模式组织方面的性能具有重要意义。

关键字

设计模式,深度学习,特征集,性能,分类器

论文作者

Shahid Hussain , Jacky Keung , Arif Ali Khan ,香港大学计算机科学系 Awais Ahmad ,大韩民国京山延南大学信息与通信工程系 Salvatore Cuomo,Francesco Piccialli, 意大利那不勒斯大学 Gwanggil Jeon , 韩国仁川国立大学嵌入式系统工程系 Adnan Akhunzada,巴基斯坦伊斯兰堡通信卫星信息技术研究所

论文翻译链接:https://pan.baidu.com/s/1P6mUE4nkt6ZNUSPFBLQBKw 提取码:0vnr

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In recent years, defect prediction, one of the major software engineering problems, has been in the focus of researchers since it has a pivotal role in estimating software errors and faulty modules. Researchers with the goal of improving prediction accuracy have developed many models for software defect prediction. However, there are a number of critical conditions and theoretical problems in order to achieve better results. In this paper, two deep learning models, Stack Sparse Auto-Encoder (SSAE) and Deep Belief Network (DBN), are deployed to classify NASA datasets, which are unbalanced and have insufficient samples. According to the conducted experiment, the accuracy for the datasets with sufficient samples is enhanced and beside this SSAE model gains better results in comparison to DBN model in the majority of evaluation metrics.

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最新论文

In recent years, defect prediction, one of the major software engineering problems, has been in the focus of researchers since it has a pivotal role in estimating software errors and faulty modules. Researchers with the goal of improving prediction accuracy have developed many models for software defect prediction. However, there are a number of critical conditions and theoretical problems in order to achieve better results. In this paper, two deep learning models, Stack Sparse Auto-Encoder (SSAE) and Deep Belief Network (DBN), are deployed to classify NASA datasets, which are unbalanced and have insufficient samples. According to the conducted experiment, the accuracy for the datasets with sufficient samples is enhanced and beside this SSAE model gains better results in comparison to DBN model in the majority of evaluation metrics.

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