Deep Machine Learning - A New Frontier in Artificial Intelligence Research [Research Frontier]

作者：Itamar Arel, Derek Rose, Thomas P. Karnowski · 发表于：IEEE Computational Intelligence Magazine · 年份：2010 · DOI：10.1109/mci.2010.938364 · 被引用次数：1119 · 研究领域：Anomaly Detection Techniques and Applications、Generative Adversarial Networks and Image Synthesis、Neural Networks and Applications

This article provides an overview of the mainstream deep learning approaches and research directions proposed over the past decade. It is important to emphasize that each approach has strengths and "weaknesses, depending on the application and context in "which it is being used. Thus, this article presents a summary on the current state of the deep machine learning field and some perspective into how it may evolve. Convolutional Neural Networks (CNNs) and Deep Belief Networks (DBNs) (and their respective variations) are focused on primarily because they are well established in the deep learning field and show great promise for future work.