  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.
