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Determining the optimal measurements to make under a cost constraint. A measurement is "optimal" when it is expected to give the most new information about the parameters of a model. Active learning is thus an application of decision theory to the process of learning. It is also known as experiment design. | Determining the optimal measurements to make under a cost constraint. A measurement is "optimal" when it is expected to give the most new information about the parameters of a model. Active learning is thus an application of decision theory to the process of learning. It is also known as experiment design. | ||
| − | Artificial Neural | + | [[Artificial Neural Networks_OldKiwi]]: Ripley Chapter 5 |
Duda, Hart, and Stork Chapter 6 | Duda, Hart, and Stork Chapter 6 | ||
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is modeled as a composition of simple functions <math>f_i</math>'s | is modeled as a composition of simple functions <math>f_i</math>'s | ||
| − | <math>f=f_n\bullet f_{n-1}\ | + | <math>f=f_n\bullet f_{n-1}\cdots f_1</math> |
Latest revision as of 10:08, 3 April 2008
Determining the optimal measurements to make under a cost constraint. A measurement is "optimal" when it is expected to give the most new information about the parameters of a model. Active learning is thus an application of decision theory to the process of learning. It is also known as experiment design.
Artificial Neural Networks_OldKiwi: Ripley Chapter 5
Duda, Hart, and Stork Chapter 6
Nueral networks are a family of function approximation techniques, when the function is approximated,
$ f:x \rightarrow z $
is modeled as a composition of simple functions $ f_i $'s
$ f=f_n\bullet f_{n-1}\cdots f_1 $
