We propose a new general model called IPNN - Indeterminate Probability Neural Network, which combines neural network and probability theory together. In the classical probability theory, the calculation of probability is based on the occurrence of events, which is hardly used in current neural networks. In this paper, we propose a new general probability theory, which is an extension of classical probability theory, and makes classical probability theory a special case to our theory. Besides, for our proposed neural network framework, the output of neural network is defined as probability events, and based on the statistical analysis of these events, the inference model for classification task is deduced. IPNN shows new property: It can perform unsupervised clustering while doing classification. Besides, IPNN is capable of making very large classification with very small neural network, e.g. model with 100 output nodes can classify 10 billion categories. Theoretical advantages are reflected in experimental results.

翻译：我们提出了一种新的通用模型——IPNN（不确定概率神经网络），该模型将神经网络与概率理论相结合。在经典概率理论中，概率计算基于事件的发生，而这一机制在当前神经网络中鲜有应用。本文提出了一种新的通用概率理论，该理论是对经典概率理论的扩展，并将经典概率理论作为其特例。此外，针对我们所提出的神经网络框架，网络的输出被定义为概率事件，基于对这些事件的统计分析，推导出了用于分类任务的推断模型。IPNN展现了一个新特性：它能在执行分类的同时进行无监督聚类。此外，IPNN能够以极小的神经网络实现大规模分类，例如，具有100个输出节点的模型即可对100亿个类别进行分类。理论上的优势在实验结果中得到了体现。