模式识别是一个成熟的、令人兴奋的、快速发展的领域,它支撑着计算机视觉、图像处理、文本和文档分析以及神经网络等相关领域的发展。它与机器学习非常相似,在生物识别、生物信息学、多媒体数据分析和最新的数据科学等新兴领域也有应用。模式识别(Pattern Recognition)杂志成立于大约50年前,当时该领域刚刚出现计算机科学的早期。在这期间,它已大大扩大。只要这些论文的背景得到了清晰的解释并以模式识别文献为基础,该杂志接受那些对模式识别理论、方法和在任何领域的应用做出原创贡献的论文。 官网地址:http://dblp.uni-trier.de/db/conf/par/

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Memristive systems and devices are potentially available for implementing reservoir computing (RC) systems applied to pattern recognition. However, the computational ability of memristive RC systems depends on intertwined factors such as system architectures and physical properties of memristive elements, which complicates identifying the key factor for system performance. Here we develop a simulation platform for RC with memristor device networks, which enables testing different system designs for performance improvement. Numerical simulations show that the memristor-network-based RC systems can yield high computational performance comparable to that of state-of-the-art methods in three time series classification tasks. We demonstrate that the excellent and robust computation under device-to-device variability can be achieved by appropriately setting network structures, nonlinearity of memristors, and pre/post-processing, which increases the potential for reliable computation with unreliable component devices. Our results contribute to an establishment of a design guide for memristive reservoirs toward a realization of energy-efficient machine learning hardware.

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Memristive systems and devices are potentially available for implementing reservoir computing (RC) systems applied to pattern recognition. However, the computational ability of memristive RC systems depends on intertwined factors such as system architectures and physical properties of memristive elements, which complicates identifying the key factor for system performance. Here we develop a simulation platform for RC with memristor device networks, which enables testing different system designs for performance improvement. Numerical simulations show that the memristor-network-based RC systems can yield high computational performance comparable to that of state-of-the-art methods in three time series classification tasks. We demonstrate that the excellent and robust computation under device-to-device variability can be achieved by appropriately setting network structures, nonlinearity of memristors, and pre/post-processing, which increases the potential for reliable computation with unreliable component devices. Our results contribute to an establishment of a design guide for memristive reservoirs toward a realization of energy-efficient machine learning hardware.

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