提出一种可动态增删类别的小型概率神经网络，无需调参即可持续学习与遗忘。
Automatic Construction of Pattern Classifiers Capable of Continuous Incremental Learning and Unlearning Tasks Based on Compact-Sized Probabilistic Neural Network
- 用单次遍历算法自动构建小型概率神经网络，无须迭代优化
- 隐藏单元数少于原模型，分类性能媲美多层感知机
- 支持连续增量与减量学习，适合需要动态更新的场景
本文提出一种基于紧凑型概率神经网络的模式分类新方法，该网络具备持续增量学习与删减任务的能力。通过无需超参数调优的单次遍历网络生长算法构建或重构网络，其结构与参数在给定训练数据集后自动确定，并可在持续增量与减量学习中动态调整。所提算法不依赖迭代或复杂的矩阵近似计算，而是采用简单数据驱动的更新机制。在九个公开数据库上的仿真结果表明，所构建的紧凑型概率神经网络隐藏单元数量远少于原始模型，但在标准分类任务中仍能达到与多层感知机相近的分类性能，同时具备足够的连续类别增量与减量学习能力。
原文摘要 · Abstract (English)
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.
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