A Very Fast Neural Learning for Classification Using Only New Incoming Datum

作者：Jaiyen Saichon^*; Lursinsap Chidchanok; Phimoltares Suphakant

来源：IEEE Transactions on Neural Networks, 2010, 21(3): 381-392.

DOI：10.1109/TNN.2009.2037148

• Classification
• clustering
• ellipsoid
• elliptic basis EBF)
• fast learning
• hyperellipsoid
• neural network
• principal component analysis (PCA)
• radial basis RBF)
• recognition

This paper proposes a very fast 1-pass-throw-away learning algorithm based on a hyperellipsoidal function that can be translated and rotated to cover the data set during learning process. The translation and rotation of hyperellipsoidal function depends upon the distribution of the data set. In addition, we present versatile elliptic basis VEBF) neural network with one hidden layer. The hidden layer is adaptively divided into subhidden layers according to the number of classes of the training data set. Each subhidden layer can be scaled by incrementing a new node to learn new samples during training process. The learning time is O(n), where n is the number of data. The network can independently learn any new incoming datum without involving the previously learned data. There is no need to store all the data in order to mix with the new incoming data during the learning process.

• 出版日期2010-3