关键词: 神经网络, 动态特征空间, 动态超球体, 标准数据集, MNIST手写体

Abstract:

In pattern recognition, samples are directly classified in the feature space or mapped to higher dimensional spaces by kernel functions. In order to improve the classification effect, better separation surface and more efficient feature space are commonly used. This paper proposes a Dynamic Hypersphere Algorithm（DHA）  for classification that can find a more efficient feature space. DHA uses dynamic feature transformation and optimizes the same type of data into the same hypersphere. This paper proves that DHA can obtain better classification results by finding effective feature space by experimenting on standard data sets. In addition, in order to further prove the validity of the feature space, DHA is applied to the MNIST handwriting by reducing the training samples and reducing the original sample from 784 dimensions to 10 dimensions, a recognition rate of 90.18% is obtained, and good results are also obtained in the unbalanced handwriting.

Key words: neural network, dynamic feature space, dynamic hypersphere, standard data sets, MNIST handwriting