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Is it a acceptable way to write a loss function in this form?
Does the Bishop book imply that a neuron feeds to itself in chapter 5.3?Loss function to maximize sum of targetsConnection between cross entropy and likelihood for multi-class soft label classificationDifferentiating roadmap of a loss functionPurpose of backpropagation in neural networksUsing SMAPE as a loss function for an LSTMPerceptron Learning RuleAn ambiguity in SVM equations about misclassified dataAmbiguity in Perceptron loss function (C. Bishop vs F. Rosenblatt)What is the relationship between “square loss” and “Mean squared error”?
.everyoneloves__top-leaderboard:empty,.everyoneloves__mid-leaderboard:empty,.everyoneloves__bot-mid-leaderboard:empty margin-bottom:0;
$begingroup$
I found a loss function of a perceptron on a book is in this form
$$ L(w,b) = - sumlimits_x_i in My_i(wx_i+b) $$
In the context of machine learning loss functions, is it acceptable to put $x_i$ on the bottom of a summation notation?
machine-learning loss-function
edited Jun 9 at 16:43
bkshi
1,1074 silver badges16 bronze badges
asked Jun 9 at 13:31
JayJay
1734 bronze badges
$endgroup$
add a comment |
$begingroup$
I found a loss function of a perceptron on a book is in this form
$$ L(w,b) = - sumlimits_x_i in My_i(wx_i+b) $$
In the context of machine learning loss functions, is it acceptable to put $x_i$ on the bottom of a summation notation?
machine-learning loss-function
edited Jun 9 at 16:43
bkshi
1,1074 silver badges16 bronze badges
asked Jun 9 at 13:31
JayJay
1734 bronze badges
$endgroup$
add a comment |
$begingroup$
I found a loss function of a perceptron on a book is in this form
$$ L(w,b) = - sumlimits_x_i in My_i(wx_i+b) $$
In the context of machine learning loss functions, is it acceptable to put $x_i$ on the bottom of a summation notation?
machine-learning loss-function
edited Jun 9 at 16:43
bkshi
1,1074 silver badges16 bronze badges
asked Jun 9 at 13:31
JayJay
1734 bronze badges
$endgroup$
I found a loss function of a perceptron on a book is in this form
$$ L(w,b) = - sumlimits_x_i in My_i(wx_i+b) $$
In the context of machine learning loss functions, is it acceptable to put $x_i$ on the bottom of a summation notation?
machine-learning loss-function
machine-learning loss-function
edited Jun 9 at 16:43
bkshi
1,1074 silver badges16 bronze badges
asked Jun 9 at 13:31
JayJay
1734 bronze badges
edited Jun 9 at 16:43
bkshi
1,1074 silver badges16 bronze badges
asked Jun 9 at 13:31
JayJay
1734 bronze badges
edited Jun 9 at 16:43
bkshi
1,1074 silver badges16 bronze badges
edited Jun 9 at 16:43
bkshi
1,1074 silver badges16 bronze badges
edited Jun 9 at 16:43
bkshi
1,1074 silver badges16 bronze badges
1,1074 silver badges16 bronze badges
asked Jun 9 at 13:31
JayJay
1734 bronze badges
asked Jun 9 at 13:31
JayJay
1734 bronze badges
asked Jun 9 at 13:31
JayJay
1734 bronze badges
1734 bronze badges
add a comment |
add a comment |
1 Answer
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$begingroup$
It just means to sum over all $x_i$ in $M$. That is completely acceptable notation.
answered Jun 9 at 16:00
DaveDave
4115 bronze badges
$endgroup$
$begingroup$
Adding to what @Dave pointed out, when optimizing a perceptron you are essentially compute the loss for each input in your training dataset and sum that. To do that you need to traverse the full training data set, which is exactly what $sum_x_i in M$ means.
$endgroup$
– thushv89
Jun 12 at 4:36
add a comment |
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1 Answer
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$begingroup$
It just means to sum over all $x_i$ in $M$. That is completely acceptable notation.
answered Jun 9 at 16:00
DaveDave
4115 bronze badges
$endgroup$
$begingroup$
Adding to what @Dave pointed out, when optimizing a perceptron you are essentially compute the loss for each input in your training dataset and sum that. To do that you need to traverse the full training data set, which is exactly what $sum_x_i in M$ means.
$endgroup$
– thushv89
Jun 12 at 4:36
add a comment |
$begingroup$
It just means to sum over all $x_i$ in $M$. That is completely acceptable notation.
answered Jun 9 at 16:00
DaveDave
4115 bronze badges
$endgroup$
$begingroup$
Adding to what @Dave pointed out, when optimizing a perceptron you are essentially compute the loss for each input in your training dataset and sum that. To do that you need to traverse the full training data set, which is exactly what $sum_x_i in M$ means.
$endgroup$
– thushv89
Jun 12 at 4:36
add a comment |
$begingroup$
It just means to sum over all $x_i$ in $M$. That is completely acceptable notation.
answered Jun 9 at 16:00
DaveDave
4115 bronze badges
$endgroup$
It just means to sum over all $x_i$ in $M$. That is completely acceptable notation.
answered Jun 9 at 16:00
DaveDave
4115 bronze badges
answered Jun 9 at 16:00
DaveDave
4115 bronze badges
answered Jun 9 at 16:00
DaveDave
4115 bronze badges
answered Jun 9 at 16:00
DaveDave
4115 bronze badges
4115 bronze badges
$begingroup$
Adding to what @Dave pointed out, when optimizing a perceptron you are essentially compute the loss for each input in your training dataset and sum that. To do that you need to traverse the full training data set, which is exactly what $sum_x_i in M$ means.
$endgroup$
– thushv89
Jun 12 at 4:36
add a comment |
$begingroup$
Adding to what @Dave pointed out, when optimizing a perceptron you are essentially compute the loss for each input in your training dataset and sum that. To do that you need to traverse the full training data set, which is exactly what $sum_x_i in M$ means.
$endgroup$
– thushv89
Jun 12 at 4:36
$begingroup$
Adding to what @Dave pointed out, when optimizing a perceptron you are essentially compute the loss for each input in your training dataset and sum that. To do that you need to traverse the full training data set, which is exactly what $sum_x_i in M$ means.
$endgroup$
– thushv89
Jun 12 at 4:36
$begingroup$
Adding to what @Dave pointed out, when optimizing a perceptron you are essentially compute the loss for each input in your training dataset and sum that. To do that you need to traverse the full training data set, which is exactly what $sum_x_i in M$ means.
$endgroup$
– thushv89
Jun 12 at 4:36
add a comment |
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