Research On RBF Neural Networks Model And Learning Algorithm Based On Semi-Supervied And Multiple-Instance

There is too much diversity and uncertainty in the data of actual productive lifestyles, dealing with diversity and vast amounts of data become the focus of present machine learning tasks. So, the models and algorithms of machine learning base on semi-supervised and multi-instance will become a hotspot research direction.Firstly, aiming at the problems of semi-supervised and multi-instance, a training algorithm of RBF neural network based on semi-supervised and multi-instance was proposed in this paper, the proposed algorithm was in non- sequential sample space, which based on the RBF neural network, and the cluster algorithm. At the same time, the paper carried on the outlier analysis in the sample space. The basic ideas of the algorithm were recommended as follows: By defining a kind of Hausdorff distance which can measure the distance between two sets, then a clustering algorithm based on semi-supervised and multi-instance is proposed.The proposed algorithm marks the unlabeled sample with the help of transcendent experience of labeled sample. So that distribution of sample space under the cluster assumption was proved up, then the method used RBF neural network to train the whole sample set, among which Hausdorff distance is used as the norm of the RBF kernel function, so as to improve network training ability of neural networks. And in order to prove practicability of the algorithm, a simulation experiment is carried out.Secondly, in sequential sample space, base on the RBF process neural networks, timing clustering algorithm and genetic algorithm, a training algorithm of RBF process neural network based on semi- supervised and multi-instance was proposed in sequential sample space, which can be regard as a general case. The basic ideas of the algorithm were recommended as follows: By defining a kind of generalized timing Hausdorff distance which was extended by the Hausdorff distance, a timing clustering algorithm based on semi-supervised and multi-instance was proposed. Then, the method used RBF process neural network to train the sample set. In the training process, the neural network needed adjusting coefficients of kernel central functions. The method introduced genetic algorithm to solving non-differentiable problem of minimal function, at the same time, owing to the global optimal property of genetic algorithm, the proposed algorithm of neural network could reduces iteration times. And the practicability of this algorithm is proved by a simulation experiment.Finally, aiming at the problem in the ineffective training of neural networks, under the mixing MPI and OpenMP program technology, a parallel training algorithm based on semi-supervised and multi-instance is proposed. In the algorithm, the parallel computation would be realized in the clustering operator and genetic operators of the RBF process neural network base on semi-supervised and multi-instance. Under different magnitude samples and compute nodes, the comparative tests are carried out. The results show that the algorithm could reduce the training time, improve the property of neural network, when the parallel granularity was appropriate.