基于紧凑型概率神经网络的自动构造可进行连续增量学习与遗忘任务的模式分类器
Automatic Construction of Pattern Classifiers Capable of Continuous Incremental Learning and Unlearning Tasks Based on Compact-Sized Probabilistic Neural Network
AI总结：
本文提出一种基于紧凑型概率神经网络的模式分类方法，通过简单的一次性网络生长算法自动确定结构和参数，实现连续增量学习与遗忘任务，实验表明其在分类性能和连续学习任务中表现优异。
摘要：
本文提出了一种基于紧凑型概率神经网络的模式分类方法。该策略基于能够进行连续增量学习和遗忘任务的紧凑型概率神经网络。网络通过简单的、一次性的网络生长算法构建或重构，无需超参数调优。给定训练数据集后，其结构和参数会自动确定，并能在持续增量和减量学习情况下动态变化。本文提出的算法不涉及迭代或繁琐的矩阵参数近似，而是采用简单的数据驱动更新方案。使用九个公开数据库的仿真结果证明了该方法的有效性，显示构造的紧凑型概率神经网络具有比原始概率神经网络模型更少的隐单元数量，却能在标准分类任务中达到与多层感知器神经网络相似的分类性能，同时在连续类增量学习和遗忘任务中也表现出足够的能力。
英文摘要：
This paper proposes a novel approach to pattern classification using a probabilistic neural network model. The strategy is based on a compact-sized probabilistic neural network capable of continuous incremental learning and unlearning tasks. The network is constructed/reconstructed using a simple, one-pass network-growing algorithm with no hyperparameter tuning. Then, given the training dataset, its structure and parameters are automatically determined and can be dynamically varied in continual incremental and decremental learning situations. The algorithm proposed in this work involves no iterative or arduous matrix-based parameter approximations but a simple data-driven updating scheme. Simulation results using nine publicly available databases demonstrate the effectiveness of this approach, showing that compact-sized probabilistic neural networks constructed have a much smaller number of hidden units compared to the original probabilistic neural network model and yet can achieve a similar classification performance to that of multilayer perceptron neural networks in standard classification tasks, while also exhibiting sufficient capability in continuous class incremental learning and unlearning tasks.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
