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Cost function
Cost function

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!!! Overview
[{$pagename}] is the average of the [Loss function] of the entire [Training dataset].
The desire is to find the parameters 𝑤 𝑎𝑛𝑑 𝑏 that minimize the overall [{$pagename}].
[Cost function/Screen Shot 2017-12-26 at 06.17.00.png]
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Where:
* w is the [weight] a [Vector]
* b is the [bias] a [Scalar]
* L is the [Loss function]
* m is number of examples in the [Training dataset]
* 𝑦̂ is the [Predictor variable] output vector. It can also be denoted a^(number of layers)
* y is the truth from [Training dataset]
[{$pagename}] sum of [Loss functions] over your [training dataset] plus some model complexity penalty.
A [loss function] is a part of a [{$pagename}] which is a type of an [objective function].
[{$pagename}] is generally represented by "J" and
The entire concept of "Training a [Artificial Neural network] is minimizing the [{$pagename}]
Normally, you must optimise the [Training dataset] and the [weights] on the [synapses] as you will __NOT__ be able to control over the input [data]
!! Common [Examples]:
* [Mean Squared Error] ([MSE]): MSE(θ)=1N∑Ni=1(f(xi|θ)−yi)2MSE(θ)=1N∑i=1N(f(xi|θ)−yi)2
* SVM cost function: SVM(θ)=‖θ‖2+C∑Ni=1ξiSVM(θ)=‖θ‖2+C∑i=1Nξi \\(there are additional constraints connecting ξiξi with CC and with [Training dataset])
* J = ∑1/2(y-yHat)exp(2)
!! Category
%%category [Artificial Intelligence]%%
!! More Information
There might be more information for this subject on one of the following:
[{ReferringPagesPlugin before='*' after='\n' }]
This page (revision-1) was last changed on 29-Nov-2024 16:16 by UnknownAuthorTop