You're not expressing an opinion, but making false claims. I'm talking about a theorem, not about ONE experiment.
BUT, it is clearly true that if you have a parametric model of your problem, you can identify the parameters and use the model to both extra and interpolate your data. Moreover, the result will in general be better than whet you will obtain with a non parametric model. So NN are not a good solution for all problems, especially when you've got a good knowledge of the problem. Your present post should have a good score because it gives valuable information about limitation of NN, whereas your first post was pure rant.
The problem is that you are comparing two different methods. One is parametric and the other is non parametric. NO non parametric model can be use to correctly extrapolate data (regardless wether it is or not based on NN). Among non parametric models, NN have been mathematically demonstrated to be the best ones.
You clearly do not understand fully what you are talking about and the so called statistician you are quoting should give you better explanation about the differences between parametric and non parametric estimation. We are talking about science and there are no opinion here, there are facts.
By the way, my vitriol is a sign I'm pissed of to see your incorrect post has been scored informative whereas it should be scored as a flame. I've got a PhD in maths and my subject was Neural Networks. I work as an assistant professor in a statistics departement. I do know what I'm talking about, but I hate using such diploma arguments.
This this the most ridiculous post I've ever read. As many replies have already explained, a Multi Layer Perceptron (one kind of NN) is an universal approximator, that is it can be used to model a mapping from one set to another, thanks to the data (a set of input-ouput associations).
There are other ways to obtain universal approximation (which means that basicaly any regular mapping can be approximately represented). NO method can be used to give correct EXTRApolations. This is not possible. Period.
Now, regarding INTERpolation of the data, Barron as demonstrated in 93 (see Universal approximation bounds for superposition of a sigmoidal function in IEEE transactions on Information Theory volume 39 number 3, pages 930-945) that MLP are more efficient than any other methods. This means that they use LESS parameters than other interpolation methodes (such as spline, kernel regression, etc.).
So, the post I'm answering to is bullshit. I strongly advise posters to read the NN FAQ before posting ridiculous claims.
MySQL does not support transactions. The good point is that there is NO way to make a transactionnal database as efficient as a non transactionnal one. So MySQL is faster than PostgreSQL for many benches.
The bad point is that when you need transaction, you have to perform table level locks which are far LESS efficient than row level locks of PostgreSQL.
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You're not expressing an opinion, but making false claims. I'm talking about a theorem, not about ONE experiment.
BUT, it is clearly true that if you have a parametric model of your problem, you can identify the parameters and use the model to both extra and interpolate your data. Moreover, the result will in general be better than whet you will obtain with a non parametric model. So NN are not a good solution for all problems, especially when you've got a good knowledge of the problem. Your present post should have a good score because it gives valuable information about limitation of NN, whereas your first post was pure rant.
The problem is that you are comparing two different methods. One is parametric and the other is non parametric. NO non parametric model can be use to correctly extrapolate data (regardless wether it is or not based on NN). Among non parametric models, NN have been mathematically demonstrated to be the best ones.
You clearly do not understand fully what you are talking about and the so called statistician you are quoting should give you better explanation about the differences between parametric and non parametric estimation. We are talking about science and there are no opinion here, there are facts.
By the way, my vitriol is a sign I'm pissed of to see your incorrect post has been scored informative whereas it should be scored as a flame. I've got a PhD in maths and my subject was Neural Networks. I work as an assistant professor in a statistics departement. I do know what I'm talking about, but I hate using such diploma arguments.
This this the most ridiculous post I've ever read. As many replies have already explained, a Multi Layer Perceptron (one kind of NN) is an universal approximator, that is it can be used to model a mapping from one set to another, thanks to the data (a set of input-ouput associations).
There are other ways to obtain universal approximation (which means that basicaly any regular mapping can be approximately represented). NO method can be used to give correct EXTRApolations. This is not possible. Period.
Now, regarding INTERpolation of the data, Barron as demonstrated in 93 (see Universal approximation bounds for superposition of a sigmoidal function in IEEE transactions on Information Theory volume 39 number 3, pages 930-945) that MLP are more efficient than any other methods. This means that they use LESS parameters than other interpolation methodes (such as spline, kernel regression, etc.).
So, the post I'm answering to is bullshit. I strongly advise posters to read the NN FAQ before posting ridiculous claims.
MySQL does not support transactions. The good point is that there is NO way to make a transactionnal database as efficient as a non transactionnal one. So MySQL is faster than PostgreSQL for many benches.
The bad point is that when you need transaction, you have to perform table level locks which are far LESS efficient than row level locks of PostgreSQL.
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