This paper extends and explains the Multiple Additive Neural Networks (MANN) methodology, an enhancement to the traditional Gradient Boosting framework, utilizing nearly shallow neural networks instead of decision trees as base learners. This innovative approach leverages neural network architectures, notably Convolutional Neural Networks (CNNs) and Capsule Neural Networks, to extend its application to both structured data and unstructured data such as images and audio. For structured data the advantages of capsule neural networks as feature extractors are used and combined with MANN as a classifier. MANN's unique architecture promotes continuous learning and integrates advanced heuristics to combat overfitting, ensuring robustness and reducing sensitivity to hyperparameter settings like learning rate and iterations. Our empirical studies reveal that MANN surpasses traditional methods such as Extreme Gradient Boosting (XGB) in accuracy across well-known datasets. This research demonstrates MANN's superior precision and generalizability, making it a versatile tool for diverse data types and complex learning environments.

翻译：本文扩展并阐释了多重加性神经网络（MANN）方法，该方法是对传统梯度提升框架的改进，采用近似浅层神经网络替代决策树作为基学习器。这一创新方法利用神经网络架构，特别是卷积神经网络（CNN）和胶囊神经网络，将其应用范围拓展至结构化数据以及图像、音频等非结构化数据。针对结构化数据，该方法利用胶囊神经网络作为特征提取器的优势，并与MANN分类器相结合。MANN的独特架构促进了持续学习，并集成了先进启发式策略以应对过拟合，从而确保鲁棒性并降低对学习率、迭代次数等超参数设置的敏感性。我们的实证研究表明，在知名数据集上，MANN在准确性方面超越了极端梯度提升（XGB）等传统方法。本研究证明了MANN卓越的精度与泛化能力，使其成为适用于多种数据类型和复杂学习环境的通用工具。