Deep Relational Machines
Deep Relational Machines
复制标题
深度关系机器
DOI：
10.1007/978-3-642-42042-9_27
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发表时间：
2013
期刊：
影响因子：
--
通讯作者：
H. Lodhi
中科院分区：
文献类型：
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作者：
H. Lodhi
深度学习方法包含了一类新的学习算法，提供了最先进的性能。我们提出了一种新的方法来学习深度架构，并将其称为深度关系机器（DRM）。DRM通过引入一阶Horn子句学习第一层表示，并利用受限玻尔兹曼机生成后续层。它的特点是能够捕获数据中包含的结构和关系信息。为了评估我们的方法，我们将其应用于具有挑战性的问题，包括蛋白质折叠识别和有毒和致突变化合物的检测。实验结果表明，我们的技术在研究中大大优于所有其他方法。
Deep learning methods that comprise a new class of learning algorithms give state-of-the-art performance. We propose a novel methodology to learn deep architectures and refer to it as a deep relational machine (DRM). A DRM learns the first layer of representation by inducing first order Horn clauses and the successive layers are generated by utilizing restricted Boltzmann machines. It is characterised by its ability to capture structural and relational information contained in data. To evaluate our approach, we apply it to challenging problems including protein fold recognition and detection of toxic and mutagenic compounds. The experimental results demonstrate that our technique substantially outperforms all other approaches in the study.