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Meaning of this notion in 0-1 loss?
Prove Reccurrent Neural Network can exhibit oscillatory behaviorPerformance and architecture of neural network for increased dimensionsReinforcement learning: understanding this derivation of n-step Tree Backup algorithmUsing ML to create unique descriptors?L2 loss vs. mean squared lossAn unbiased simulator for policy simulation in reinforcement learningIs it possible to use NEAT networks for solving video games?Why is my loss function for DQN converging too quickly?Why Gradient methods work in finding the parameters in Neural Networks?Splitting image dataset with few subjects but many data
$begingroup$
I am reading a paper and encountered this notion:
$$1_Y=1$$
To me it seems to be the expression as below, but I am not entirely sure and I don't think the author explictly explained it:
if Y==1:
return 1
else:
return 0
Can someone help me to clarify this notion? Much thanks for your time (:
It appears in:
https://papers.nips.cc/paper/5073-learning-with-noisy-labels.pdf
machine-learning loss-function math
New contributor
$endgroup$
add a comment |
$begingroup$
I am reading a paper and encountered this notion:
$$1_Y=1$$
To me it seems to be the expression as below, but I am not entirely sure and I don't think the author explictly explained it:
if Y==1:
return 1
else:
return 0
Can someone help me to clarify this notion? Much thanks for your time (:
It appears in:
https://papers.nips.cc/paper/5073-learning-with-noisy-labels.pdf
machine-learning loss-function math
New contributor
$endgroup$
$begingroup$
Your assumption is right.
$endgroup$
– Alireza Zolanvari
Mar 20 at 10:19
add a comment |
$begingroup$
I am reading a paper and encountered this notion:
$$1_Y=1$$
To me it seems to be the expression as below, but I am not entirely sure and I don't think the author explictly explained it:
if Y==1:
return 1
else:
return 0
Can someone help me to clarify this notion? Much thanks for your time (:
It appears in:
https://papers.nips.cc/paper/5073-learning-with-noisy-labels.pdf
machine-learning loss-function math
New contributor
$endgroup$
I am reading a paper and encountered this notion:
$$1_Y=1$$
To me it seems to be the expression as below, but I am not entirely sure and I don't think the author explictly explained it:
if Y==1:
return 1
else:
return 0
Can someone help me to clarify this notion? Much thanks for your time (:
It appears in:
https://papers.nips.cc/paper/5073-learning-with-noisy-labels.pdf
machine-learning loss-function math
machine-learning loss-function math
New contributor
New contributor
edited Mar 20 at 11:57
Siong Thye Goh
1,387519
1,387519
New contributor
asked Mar 20 at 9:49
J. DoeJ. Doe
82
82
New contributor
New contributor
$begingroup$
Your assumption is right.
$endgroup$
– Alireza Zolanvari
Mar 20 at 10:19
add a comment |
$begingroup$
Your assumption is right.
$endgroup$
– Alireza Zolanvari
Mar 20 at 10:19
$begingroup$
Your assumption is right.
$endgroup$
– Alireza Zolanvari
Mar 20 at 10:19
$begingroup$
Your assumption is right.
$endgroup$
– Alireza Zolanvari
Mar 20 at 10:19
add a comment |
1 Answer
1
active
oldest
votes
$begingroup$
Your understanding is correct.
This is known as the indicator function.
The indicator function of a subset $A$ of a set $X$ is a function
$$1_A(x)= begincases1, & x in A \ 0, & x notin A endcases$$
$endgroup$
$begingroup$
Also since both $I_Y=1$ and $I_y=1$ are present in the question. It is worth noting that $Z=I_Y=1$ is a random variable like $Z=2Y$ with a distribution, unlike $f(y)=I_y=1$ which is a function with no distribution.
$endgroup$
– Esmailian
Mar 20 at 13:04
add a comment |
Your Answer
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1 Answer
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1 Answer
1
active
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oldest
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oldest
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$begingroup$
Your understanding is correct.
This is known as the indicator function.
The indicator function of a subset $A$ of a set $X$ is a function
$$1_A(x)= begincases1, & x in A \ 0, & x notin A endcases$$
$endgroup$
$begingroup$
Also since both $I_Y=1$ and $I_y=1$ are present in the question. It is worth noting that $Z=I_Y=1$ is a random variable like $Z=2Y$ with a distribution, unlike $f(y)=I_y=1$ which is a function with no distribution.
$endgroup$
– Esmailian
Mar 20 at 13:04
add a comment |
$begingroup$
Your understanding is correct.
This is known as the indicator function.
The indicator function of a subset $A$ of a set $X$ is a function
$$1_A(x)= begincases1, & x in A \ 0, & x notin A endcases$$
$endgroup$
$begingroup$
Also since both $I_Y=1$ and $I_y=1$ are present in the question. It is worth noting that $Z=I_Y=1$ is a random variable like $Z=2Y$ with a distribution, unlike $f(y)=I_y=1$ which is a function with no distribution.
$endgroup$
– Esmailian
Mar 20 at 13:04
add a comment |
$begingroup$
Your understanding is correct.
This is known as the indicator function.
The indicator function of a subset $A$ of a set $X$ is a function
$$1_A(x)= begincases1, & x in A \ 0, & x notin A endcases$$
$endgroup$
Your understanding is correct.
This is known as the indicator function.
The indicator function of a subset $A$ of a set $X$ is a function
$$1_A(x)= begincases1, & x in A \ 0, & x notin A endcases$$
answered Mar 20 at 11:56
Siong Thye GohSiong Thye Goh
1,387519
1,387519
$begingroup$
Also since both $I_Y=1$ and $I_y=1$ are present in the question. It is worth noting that $Z=I_Y=1$ is a random variable like $Z=2Y$ with a distribution, unlike $f(y)=I_y=1$ which is a function with no distribution.
$endgroup$
– Esmailian
Mar 20 at 13:04
add a comment |
$begingroup$
Also since both $I_Y=1$ and $I_y=1$ are present in the question. It is worth noting that $Z=I_Y=1$ is a random variable like $Z=2Y$ with a distribution, unlike $f(y)=I_y=1$ which is a function with no distribution.
$endgroup$
– Esmailian
Mar 20 at 13:04
$begingroup$
Also since both $I_Y=1$ and $I_y=1$ are present in the question. It is worth noting that $Z=I_Y=1$ is a random variable like $Z=2Y$ with a distribution, unlike $f(y)=I_y=1$ which is a function with no distribution.
$endgroup$
– Esmailian
Mar 20 at 13:04
$begingroup$
Also since both $I_Y=1$ and $I_y=1$ are present in the question. It is worth noting that $Z=I_Y=1$ is a random variable like $Z=2Y$ with a distribution, unlike $f(y)=I_y=1$ which is a function with no distribution.
$endgroup$
– Esmailian
Mar 20 at 13:04
add a comment |
J. Doe is a new contributor. Be nice, and check out our Code of Conduct.
J. Doe is a new contributor. Be nice, and check out our Code of Conduct.
J. Doe is a new contributor. Be nice, and check out our Code of Conduct.
J. Doe is a new contributor. Be nice, and check out our Code of Conduct.
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$begingroup$
Your assumption is right.
$endgroup$
– Alireza Zolanvari
Mar 20 at 10:19