卷积神经网络研究综述

G. Sarker
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引用次数: 6

摘要

实现人工智能和机器学习系统的主要工具是符号人工智能和人工神经网络(ANN)人工智能。人工神经网络在机器学习(ML)的通用领域取得了巨大的进步。人工神经网络是大量加权互联人工神经元的集合,最初是在生物神经元的启发下发明的。就性能而言,这些模型比之前使用Symbolic AI实现的模型要好得多。人工神经网络的一个革命性变化是卷积神经网络(CNN)。这些结构主要适用于图像内复杂的模式识别任务。在这里,我们将讨论人工神经网络作为复杂模式识别和图像处理任务工具的基础知识。在CNN工具的一些应用中,我们将介绍基于OCR的文本翻译和基于生物识别的单模态和多模态人识别系统。
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A Survey on Convolution Neural Networks
Major tools to implement any Artificial Intelligence and Machine Learning systems are Symbolic AI and Artificial Neural Network (ANN) AI. ANN has made a dramatic improvement in the versatile area of Machine Learning (ML). ANN is a gathering of vast number of weighted interconnected artificial neurons, initially invented with the inspiration of biological neurons. These models are much better than previous models implemented with Symbolic AI so far as their performance is concerned. One revolutionary change in ANN is Convolution Neural Network (CNN). These structures are mainly suitable for complex pattern recognition tasks within images. Here we would discuss basics of ANN as a tool for complex pattern recognition and image processing task. Also some applications of the CNN tool we will present OCR based text translation and biometric based uni modal and multimodal person identification systems.
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