Skip to main content
Data Science

Return to Question

added 24 characters in body
Source Link
Green Falcon
Green Falcon
  • 14.2k
  • 10
  • 58
  • 98

In linear regression we use the following cost function which is a convex function:
enter image description here
We Use the following cost function
enter image description here
in logistic regression because the preceding cost function is not convex whenever the hypothesis (h) is logistic function. We have changed the equation of cost function to have a convex shape to find its global (andthe only the one which existexists). There is a fact that I can not understand. In Multi Layer Perceptrons ANNs I have seen alota lot that they can be tappedstuck in local minimums. Why is that? We have used this cost function for each perceptron and gotten the rules for updating the values for the weights;weights in back propagation algorithm; So why do we are trappedstuck?

In linear regression we use the following cost function which is a convex function:
enter image description here
We Use the following cost function
enter image description here
in logistic regression because the preceding cost function is not convex whenever the hypothesis (h) is logistic function. We have changed the equation of cost function to have a convex shape to find its global (and only the one which exist). There is a fact that I can not understand. In Multi Layer Perceptrons ANNs I have seen alot that they can be tapped in local minimums. Why is that? We have used this cost function for each perceptron and gotten the rules for updating the values for the weights; So why we are trapped?

In linear regression we use the following cost function which is a convex function:
enter image description here
We Use the following cost function
enter image description here
in logistic regression because the preceding cost function is not convex whenever the hypothesis (h) is logistic function. We have changed the equation of cost function to have a convex shape to find its global (the only one which exists). There is a fact that I can not understand. In Multi Layer Perceptrons ANNs I have seen a lot that they can be stuck in local minimums. Why is that? We have used this cost function for each perceptron and gotten the rules for updating the values for the weights in back propagation algorithm; So why do we stuck?

Source Link
Green Falcon
Green Falcon
  • 14.2k
  • 10
  • 58
  • 98

Does MLP always find local minimum

In linear regression we use the following cost function which is a convex function:
enter image description here
We Use the following cost function
enter image description here
in logistic regression because the preceding cost function is not convex whenever the hypothesis (h) is logistic function. We have changed the equation of cost function to have a convex shape to find its global (and only the one which exist). There is a fact that I can not understand. In Multi Layer Perceptrons ANNs I have seen alot that they can be tapped in local minimums. Why is that? We have used this cost function for each perceptron and gotten the rules for updating the values for the weights; So why we are trapped?