简要介绍了深度信念网络并介绍了一个新的面向对象工具箱（DeeBNet）
A brief survey on deep belief networks and introducing a new object oriented toolbox (DeeBNet)
摘要：
如今，在机器学习中使用深度架构非常流行。深度置信网络（DBN）是使用受限玻尔兹曼机（RBM）堆栈来使用训练数据创建强大的生成模型的深度架构。DBN具有特征提取和分类等多种功能，广泛应用于图像处理、语音处理等领域。本文介绍了一种新的面向对象的MATLAB工具箱，它具有实现DBN所需的大部分功能。在新版本中，工具箱可以在Octave中使用。根据在MNIST（图像）、ISOLET（语音）和20个新闻组（文本）数据集上进行的实验结果，表明该工具箱可以从未标记的数据中自动学习输入的良好表示，并在不同类别之间具有更好的区分。此外，在所有数据集上，所获得的分类误差与最先进的分类器的分类误差相当。此外，该工具箱支持不同的采样方法（例如Gibbs，CD，PCD和我们的新FEPCD方法），不同的稀疏方法（二次，率失真和我们的新正常方法），不同的RBM类型（生成和判别），使用GPU等。http://ceit.aut.ac.ir/~keyvanrad/DeeBNet%20Toolbox.html
英文摘要：
Nowadays, this is very popular to use the deep architectures in machine learning. Deep Belief Networks (DBNs) are deep architectures that use stack of Restricted Boltzmann Machines (RBM) to create a powerful generative model using training data. DBNs have many ability like feature extraction and classification that are used in many applications like image processing, speech processing and etc. This paper introduces a new object oriented MATLAB toolbox with most of abilities needed for the implementation of DBNs. In the new version, the toolbox can be used in Octave. According to the results of the experiments conducted on MNIST (image), ISOLET (speech), and 20 Newsgroups (text) datasets, it was shown that the toolbox can learn automatically a good representation of the input from unlabeled data with better discrimination between different classes. Also on all datasets, the obtained classification errors are comparable to those of state of the art classifiers. In addition, the toolbox supports different sampling methods (e.g. Gibbs, CD, PCD and our new FEPCD method), different sparsity methods (quadratic, rate distortion and our new normal method), different RBM types (generative and discriminative), using GPU, etc. The toolbox is a user-friendly open source software and is freely available on the website http://ceit.aut.ac.ir/~keyvanrad/DeeBNet%20Toolbox.html .
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
