A Very Fast Neural Learning for Classification
Using Only New Incoming Datum
Abstract -- 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 function (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.
Reference
Jaiyen, S., Lursinsap, C., & Phimoltares, S. (2010). A very fast neural learning for classification using only new incoming datum. IEEE Trans. Neural Netw., 21(3), 381-392.
Results/Findings
1.Versatile Elliptic Basis Function (VEBF) neural network with one hidden layer, which is a new method for classification problem, is proposed.
2. The learning time of this method is O(n) (called big O), where n is the number of data.
3. There is no need to store the whole previous data in order to mix with
the new incoming data during the learning process.
1.Versatile Elliptic Basis Function (VEBF) neural network with one hidden layer, which is a new method for classification problem, is proposed.
2. The learning time of this method is O(n) (called big O), where n is the number of data.
3. There is no need to store the whole previous data in order to mix with
the new incoming data during the learning process.
Citations
1. According to Jaiyen et al. (2010), the versatile elliptic basis function can
learn the data set very fast and save a data storage during the learning
process (p.381).
2. Jaiyen et al. (2010) present that "[the versatile elliptic basis function does not]
need to store all the data in order to mix with the new incoming data
during the learning process" (p.381).
My comments on my friends' blog.
ReplyDeletehttp://cuthesiswritig.blogspot.com/2015/01/assignment-1-abstract_22.html?showComment=1422104634440#c4152622435261845690
http://edwardkrit.blogspot.com/2015/01/citation.html?showComment=1422105219725#c4079200467781463833
Don't worry, my style isn't better than your. I just writing in my own style.
ReplyDeleteJaiyen et al. (2010) proposed a new method; Versatile Elliptic Basis Function (VEBF) neural network with one hidden layer for classification problem. This method didn’t need to store all the data in order to mix with the new incoming data during the learning process (p.381).
thank you for your opinion. ^ ^
DeleteGood start! Make sure you check articles, like "a data storage" and the reporting verb "present" is rather odd.
ReplyDelete