User talk:Kumarsanketjha

–The counterpropagation network is a hybrid network. It consists of an outstar network and a competitive filter network. It was developed in 1986 by Robert Hecht-Nielsen. It is guaranteed to find the correct weights, unlike regular back propagation networks that can become trapped in local minimums during training.

The input layer neurodes connect to each neurode in the hidden layer. The hidden layer is a Kohonen network which categorizes the pattern that was input. The output layer is an outstar array which reproduces the correct output pattern for the category.

Training is done in two stages. The hidden layer is first taught to categorize the patterns and the weights are then fixed for that layer. Then the output layer is trained. Each pattern that will be input needs a unique node in the hidden layer, which is often too large to work on real world problems.

A neural network system has been developed for rapid and accurate classification of ribosomal RNA sequences according to phylogenetic relationship. The molecular sequences are encoded into neural input vectors using an n-gram hashing method. A SVD (singular value decomposition) method is used to compress and reduce the size of long and sparse ngram input vectors. The neural networks used are three-layered, feed-forward networks that employ supervised learning paradigms, including the backpropagation algorithm and a modified counterpropagation algorithm. A pedagogical pattern selection strategy is used to reduce the training time. After trained with ribosomal RNA sequences of the RDP (Ribosomal Database Project) database, the system can classify query sequences into more than one hundred phylogenetic classes with a 100% accuracy at a rate of less than 0.3 CPU second per sequence on a workstation. When compared to other sequence similarity search methods, including Similarity Rank, Blast and Fasta, the neural network method has a higher classification accuracy at a speed of about an order of magnitude faster. The software tool will be made available to the biology community, and the system may be extended into a gene identification system for classifying indiscriminately sequenced DNA fragments.

May 2010
Hello, and thank you for your contributions to Wikipedia. I've noticed that you have been adding your signature to some of your article contributions. This is a simple mistake to make and is easy to correct. For future reference, the need to associate edits with users is taken care of by an article's edit history. Therefore, you should use your signature only when contributing to talk pages, the Village Pump, or other such discussion pages. For a better understanding of what distinguishes articles from these type of pages, please see What is an article?. Again, thank you for contributing, and enjoy your Wikipedia experience! Thank you. Salvio ( Let's talk 'bout it!) 15:24, 22 May 2010 (UTC)