NounPlural neural networks neural network (plural neural networks)
Synonyms
From Wiktionary under the GNU Free Documentation License. Traditionally, the term neural network had been used to refer to a network or circuit of biological neurons. The modern usage of the term often refers to artificial neural networks, which are composed of artificial neurons or nodes. Thus the term has two distinct usages:
This article focuses on the relationship between the two concepts; for detailed coverage of the two different concepts refer to the separate articles: Biological neural network and Artificial neural network. From Wikipedia under the
GNU Free Documentation License Idea for a paper in which I examine neural network concept learning VS other concept teory? Q. Connectionism vs Prototype theory Connectionism vs Classical view Connectionism vs Exemplar vierw Connectionism vs knowledge view (or Theory of Theory view) Connectionism vs atomis view thx Asked by kpietro - Mon Jul 14 10:46:14 2008 - - 1 Answers - 0 Comments A. Whatever you choose, be certain to make clear that the 5 senses are the "interface" between empirical reality and cognitive faculties. If you are weighted to heavily toward empiricism or rationalism, it won't matter how much "neural network concept learning" plays a part in concepual development. I have no doubt that all learning goes through a "neural network." And perhaps like the baseball player who dreams himself to sleep hitting the perfect ball, then goes out the next day and hits it, it really works. But it won't work effectively, if at all, if the conceptual base of metaphysical existents on which the process is based is faulty. Answered by Yaoi Shonen-ai - Mon Jul 14 11:08:39 2008 What is perceptron coefficient in neural network? Q. The aim of my project is to transfer compressed text data over internet and recover the data losslessly at the receiving end using ANN predictors. Text: Neural networkASCII value of Text: 78,101,117,114,97,108,32, 110,101,116,119,111,114,1 07First number:78 Residues: 23(101-78), 16(117-101),-3,-17, 76,-76, 78,-9, 15, 3,-8, 3,-7 Now i have to train the neural network to predict residue value.so for example if we take first 12 residue values the ANN has to predict the next, that is, 13th residue value. Once residues are recollected along with the first original number, predictor coefficients will be used to generate the original data series. Using the predictor coefficients an ANN identical to the ANN at the source is created.I want to… [cont.] Asked by pinku_27babypink - Sun Aug 5 10:26:59 2007 - - 1 Answers - 0 Comments A. the "coefficient" in a neural network refers to a value which is used to determine how important a certain input is to a node. In other words, the coefficients help determine whether a certain input value is considered important to the node or not. If the coefficient is strong (very far for 0, positively or negatively), then the input is considered to be very important to that node. There is one coefficient for every input. Answered by kae_verens - Sun Aug 5 10:58:34 2007 Do Neural Networks in Sports Predictions work?
Q. Does using a Neural Network in sports prediction work well? I understand it can work to some extent, but can it actually beat the odds of bookmakers consistently if used correctly with adequate historical data? If you dont know what Neural Networks are, please dont answer to point score, wiki it. Asked by Codo - Mon Jun 2 11:28:27 2008 - - 1 Answers - 0 Comments A. They are still only as good as the information put into them. If a human brain cannot predict the actual future, then neither can a neural network -- even if that neural net is as 'perfect' as a human brain. Can a neural net improve the odds of a prediction? yes and no. Yes, because it will not be influenced by emotion, or 'homerism'. It will work with, 'just the facts'. If the formulas that are used are very good and the data is very good, the neural net can actually learn as time goes on and it can have a high rate of success (but not even a neural net can be perfect). No, because of the same argument above -- it is only as good as the formulas and data you provide it with. It will make mistakes in predictions. It could be… [cont.] Answered by tlbs101 - Mon Jun 2 12:32:56 2008 From Yahoo Answer Search: "neural network"  freebooksource.info freebook hu, 12 Nov 2009 21:10:59 GM Artificial . neural networks. are used to model systems that receive inputs and produce outputs. The relationships between the inputs and outputs and the representation parameters are critical issues in the design of related engineering ...  mathworks.com Alaa Eleyan Wed, 21 May 2008 04:00:01 GM Description. Simple tutorial on pattern recognition using back propagation . neural networks. . the program has 3 classes with 3 images per class. Required Products, . Neural Network. Toolbox. MATLAB release, MATLAB 7.1.0 (R14SP3) ...  organisedchaos.org admin Mon, 26 Oct 2009 05:55:27 GM Quick and dirty . neural. nets simulation. I'm pretty sure i didn't get the physics of the . neural networks. right, and i will work on this more later, but it's. From Google Blog Search: "neural network" |
