Sagheer Alaa, Zidan Mohammed, Abdelsamea Mohammed M
College of Computer Science and Information Technology, King Faisal University, AlAhsa 31982, Saudi Arabia.
Center for Artificial Intelligence and Robotics (CAIRO), Faculty of Science, Aswan University, Aswan 81528, Egypt.
Entropy (Basel). 2019 Aug 6;21(8):763. doi: 10.3390/e21080763.
Pattern classification represents a challenging problem in machine learning and data science research domains, especially when there is a limited availability of training samples. In recent years, artificial neural network (ANN) algorithms have demonstrated astonishing performance when compared to traditional generative and discriminative classification algorithms. However, due to the complexity of classical ANN architectures, ANNs are sometimes incapable of providing efficient solutions when addressing complex distribution problems. Motivated by the mathematical definition of a quantum bit (qubit), we propose a novel autonomous perceptron model (APM) that can solve the problem of the architecture complexity of traditional ANNs. APM is a nonlinear classification model that has a simple and fixed architecture inspired by the computational superposition power of the qubit. The proposed perceptron is able to construct the activation operators autonomously after a limited number of iterations. Several experiments using various datasets are conducted, where all the empirical results show the superiority of the proposed model as a classifier in terms of accuracy and computational time when it is compared with baseline classification models.
模式分类在机器学习和数据科学研究领域中是一个具有挑战性的问题，尤其是在训练样本有限的情况下。近年来，与传统的生成式和判别式分类算法相比，人工神经网络（ANN）算法展现出了惊人的性能。然而，由于经典ANN架构的复杂性，在处理复杂分布问题时，ANN有时无法提供有效的解决方案。受量子比特（qubit）数学定义的启发，我们提出了一种新颖的自主感知机模型（APM），它可以解决传统ANN架构复杂性的问题。APM是一种非线性分类模型，其具有受qubit计算叠加能力启发的简单且固定的架构。所提出的感知机能够在有限次数的迭代后自主构建激活算子。我们使用各种数据集进行了多项实验，所有实证结果均表明，与基线分类模型相比，所提出的模型作为分类器在准确性和计算时间方面具有优越性。
