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On another random note, was anyone else dissapointed when they found out that a neural net is a fancy way of drawing a line through some points. i.e. regression.
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The neural network doesn't "know" that it is dealing with anything, just as you don't "know" the function your body uses to expand and contract your heart. You could be feeding it stock quotes, rgb pixel values, your daily weight, anything. If the information can reduced to numeric values (it can) the network will determine the relationship in the form of a function.
If the input is numbers then what happens is simply extrapolation (or regression etc). If the input is symbols, then what? The fact that you can associate numbers to the symbols does not help. The extrapolation will be meaningless. Unless you happen to have the correspondence '1'->1, '2'->2, etc,
Earlier quoted context omitted.
If the input is numbers then what happens is simply extrapolation (or regression etc). If the input is symbols, then what? The fact that you can associate numbers to the symbols does not help. The extrapolation will be meaningless. Unless you happen to have the correspondence '1'->1, '2'->2, etc,
How are you defining symbols? How are you defining meaningless, or meaning at all for that matter?
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How are you defining symbols? How are you defining meaningless, or meaning at all for that matter?
Suppose you have a function f of two variables and all you know about it is that f(x,y)=x+y for a few values of x,y. With this data, the computer extrapolates linearly and finds f(x,y) for _all_ values of x,y. The computer just "learnt" how to add! If all you have is symbols, however, you have to have numbers associated with those symbols. But you can't expect 1 correspond to '1' and 2 to '2'. As a result, your funct…
By the way, for the addition example , the neural network needs only one example, e.g. 1+1=2, applied repeatedly to discover the addition of any two inputs. There is no extrapolation between examples needed in this case.
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You can always encode symbols as numbers, and otherwise. That's what computers do, that's what you do when you do a sum by yourself, and that's what such a neural network should do too. (You should encode the symbols to a binary input, then decode the neural network output to symbols).
So given a function with f('1','1')='2', the computer will figure out that f('1','2')='3', right?
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No, I'm saying that a neural network can learn addition. See, a bayesian cannot really understand that there are relations between two numbers (like "is bigger than" or "is the following number of"), but a neural network can, since addition is part of a NN. My personal recommendation on machine learning is 'Pattern Recognition and Machine Learning' by Chris Bishop. But you definately do need a solid mathematical back…
Where would the idea of "is bigger than" or "is the following number of" come from if not from the person who creates the network?
Earlier quoted context omitted.
Suppose you have a function f of two variables and all you know about it is that f(x,y)=x+y for a few values of x,y. With this data, the computer extrapolates linearly and finds f(x,y) for _all_ values of x,y. The computer just "learnt" how to add! If all you have is symbols, however, you have to have numbers associated with those symbols. But you can't expect 1 correspond to '1' and 2 to '2'. As a result, your funct…
So am I correct in assuming that you are looking for a machine that can do the mapping of a symbol into a form that the function can learn from? Your visual and auditory systems do this, and since they are physical systems this mapping is computable. By the way, for the addition example , the neural network needs only one example, e.g. 1+1=2, applied repeatedly to discover the addition of any two inputs. There is no…
Earlier quoted context omitted.
So am I correct in assuming that you are looking for a machine that can do the mapping of a symbol into a form that the function can learn from? Your visual and auditory systems do this, and since they are physical systems this mapping is computable. By the way, for the addition example , the neural network needs only one example, e.g. 1+1=2, applied repeatedly to discover the addition of any two inputs. There is no…
"Extrapolation is the process of constructing new data points outside a discrete set of known data points."