Why is a lower bound necessary in proofs of VC-dimensions for various examples of hypotheses?Performance and architecture of neural network for increased dimensionsWhy CNN doesn't give higher accuracy over simple MLP network? [From Keras examples]Multilabel image classification: is it necessary to have traning data for each combination of labels?Why is it necessary to clean/preprocess data if the unseen real data is bound to consist missing values, outliers etc.?Correct learning algorithm for grouped examplesHow to choose negative examples for recommendation system?Why derivatives are accumulated over all examples in back propagation?CNN: Should I include null examples in the training set for image segmentationis it necessary for Artificial NN to be fully connected or only fully connected NN is called ANN?
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Why is a lower bound necessary in proofs of VC-dimensions for various examples of hypotheses?
Performance and architecture of neural network for increased dimensionsWhy CNN doesn't give higher accuracy over simple MLP network? [From Keras examples]Multilabel image classification: is it necessary to have traning data for each combination of labels?Why is it necessary to clean/preprocess data if the unseen real data is bound to consist missing values, outliers etc.?Correct learning algorithm for grouped examplesHow to choose negative examples for recommendation system?Why derivatives are accumulated over all examples in back propagation?CNN: Should I include null examples in the training set for image segmentationis it necessary for Artificial NN to be fully connected or only fully connected NN is called ANN?
$begingroup$
In the book "Foundations of Machine Learning" there are examples of proving the VC dimensions for various hypotheses, e.g., for axis-aligned rectangles, convex polygons, sine functions, hyperplanes, etc.
All proofs first derive a lower bound, and then show an upper bound. However, why not just derive the upper bound since the definition of VC dimension only cares about the "largest" set that can be shattered by hypothesis set $mathcalH$? Since all examples ends up with a lower bound matching the upper bound, is the lower bound just helpful/useful to set a target when trying to show an upper bound?
Reference:
From page 41 of this book pdf version
https://pdfs.semanticscholar.org/e923/9469aba4bccf3e36d1c27894721e8dbefc44.pdf
machine-learning pac-learning vc-theory
$endgroup$
add a comment |
$begingroup$
In the book "Foundations of Machine Learning" there are examples of proving the VC dimensions for various hypotheses, e.g., for axis-aligned rectangles, convex polygons, sine functions, hyperplanes, etc.
All proofs first derive a lower bound, and then show an upper bound. However, why not just derive the upper bound since the definition of VC dimension only cares about the "largest" set that can be shattered by hypothesis set $mathcalH$? Since all examples ends up with a lower bound matching the upper bound, is the lower bound just helpful/useful to set a target when trying to show an upper bound?
Reference:
From page 41 of this book pdf version
https://pdfs.semanticscholar.org/e923/9469aba4bccf3e36d1c27894721e8dbefc44.pdf
machine-learning pac-learning vc-theory
$endgroup$
add a comment |
$begingroup$
In the book "Foundations of Machine Learning" there are examples of proving the VC dimensions for various hypotheses, e.g., for axis-aligned rectangles, convex polygons, sine functions, hyperplanes, etc.
All proofs first derive a lower bound, and then show an upper bound. However, why not just derive the upper bound since the definition of VC dimension only cares about the "largest" set that can be shattered by hypothesis set $mathcalH$? Since all examples ends up with a lower bound matching the upper bound, is the lower bound just helpful/useful to set a target when trying to show an upper bound?
Reference:
From page 41 of this book pdf version
https://pdfs.semanticscholar.org/e923/9469aba4bccf3e36d1c27894721e8dbefc44.pdf
machine-learning pac-learning vc-theory
$endgroup$
In the book "Foundations of Machine Learning" there are examples of proving the VC dimensions for various hypotheses, e.g., for axis-aligned rectangles, convex polygons, sine functions, hyperplanes, etc.
All proofs first derive a lower bound, and then show an upper bound. However, why not just derive the upper bound since the definition of VC dimension only cares about the "largest" set that can be shattered by hypothesis set $mathcalH$? Since all examples ends up with a lower bound matching the upper bound, is the lower bound just helpful/useful to set a target when trying to show an upper bound?
Reference:
From page 41 of this book pdf version
https://pdfs.semanticscholar.org/e923/9469aba4bccf3e36d1c27894721e8dbefc44.pdf
machine-learning pac-learning vc-theory
machine-learning pac-learning vc-theory
asked Apr 10 at 0:44
ML studentML student
594
594
add a comment |
add a comment |
1 Answer
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$begingroup$
Lets use the quotes from the book.
To give an upper bound, we need to prove that no set $S$ of cardinality
$d + 1$ can be shattered by $H$
By doing this, we are proving that $textVCdim(H) < d+1$, but it does not necessarily mean $textVCdim(H) = d$. For example, we may go for an easier-to-prove failure like sets with $2d+1$ members, and consequently prove $textVCdim(H) < 2d+1$, although we know $textVCdim(H) = d$. Therefore:
To give a lower bound $d$ for $textVCdim(H)$, it suffices to show that a set $S$
of cardinality $d$ can be shattered by $H$
We need to prove the lower bound as well to show that $d le textVCdim(H) < d+1$, which implies $textVCdim(H) = d$. The last equality means $H$ can shatter any set $S$ of size $1$ to $d$.
Note that for the $2d+1$ example, we will fail to prove $2d le textVCdim(H)$, therefore $textVCdim(H) neq 2d$, and consequently we must try to prove a smaller (probably harder to prove) upper bound $d+1$.
$endgroup$
$begingroup$
Thank you!! Now I understand I had some misunderstandings of the equations.
$endgroup$
– ML student
Apr 12 at 14:04
$begingroup$
@MLstudent My pleasure! Glad I could help.
$endgroup$
– Esmailian
Apr 12 at 14:10
add a comment |
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$begingroup$
Lets use the quotes from the book.
To give an upper bound, we need to prove that no set $S$ of cardinality
$d + 1$ can be shattered by $H$
By doing this, we are proving that $textVCdim(H) < d+1$, but it does not necessarily mean $textVCdim(H) = d$. For example, we may go for an easier-to-prove failure like sets with $2d+1$ members, and consequently prove $textVCdim(H) < 2d+1$, although we know $textVCdim(H) = d$. Therefore:
To give a lower bound $d$ for $textVCdim(H)$, it suffices to show that a set $S$
of cardinality $d$ can be shattered by $H$
We need to prove the lower bound as well to show that $d le textVCdim(H) < d+1$, which implies $textVCdim(H) = d$. The last equality means $H$ can shatter any set $S$ of size $1$ to $d$.
Note that for the $2d+1$ example, we will fail to prove $2d le textVCdim(H)$, therefore $textVCdim(H) neq 2d$, and consequently we must try to prove a smaller (probably harder to prove) upper bound $d+1$.
$endgroup$
$begingroup$
Thank you!! Now I understand I had some misunderstandings of the equations.
$endgroup$
– ML student
Apr 12 at 14:04
$begingroup$
@MLstudent My pleasure! Glad I could help.
$endgroup$
– Esmailian
Apr 12 at 14:10
add a comment |
$begingroup$
Lets use the quotes from the book.
To give an upper bound, we need to prove that no set $S$ of cardinality
$d + 1$ can be shattered by $H$
By doing this, we are proving that $textVCdim(H) < d+1$, but it does not necessarily mean $textVCdim(H) = d$. For example, we may go for an easier-to-prove failure like sets with $2d+1$ members, and consequently prove $textVCdim(H) < 2d+1$, although we know $textVCdim(H) = d$. Therefore:
To give a lower bound $d$ for $textVCdim(H)$, it suffices to show that a set $S$
of cardinality $d$ can be shattered by $H$
We need to prove the lower bound as well to show that $d le textVCdim(H) < d+1$, which implies $textVCdim(H) = d$. The last equality means $H$ can shatter any set $S$ of size $1$ to $d$.
Note that for the $2d+1$ example, we will fail to prove $2d le textVCdim(H)$, therefore $textVCdim(H) neq 2d$, and consequently we must try to prove a smaller (probably harder to prove) upper bound $d+1$.
$endgroup$
$begingroup$
Thank you!! Now I understand I had some misunderstandings of the equations.
$endgroup$
– ML student
Apr 12 at 14:04
$begingroup$
@MLstudent My pleasure! Glad I could help.
$endgroup$
– Esmailian
Apr 12 at 14:10
add a comment |
$begingroup$
Lets use the quotes from the book.
To give an upper bound, we need to prove that no set $S$ of cardinality
$d + 1$ can be shattered by $H$
By doing this, we are proving that $textVCdim(H) < d+1$, but it does not necessarily mean $textVCdim(H) = d$. For example, we may go for an easier-to-prove failure like sets with $2d+1$ members, and consequently prove $textVCdim(H) < 2d+1$, although we know $textVCdim(H) = d$. Therefore:
To give a lower bound $d$ for $textVCdim(H)$, it suffices to show that a set $S$
of cardinality $d$ can be shattered by $H$
We need to prove the lower bound as well to show that $d le textVCdim(H) < d+1$, which implies $textVCdim(H) = d$. The last equality means $H$ can shatter any set $S$ of size $1$ to $d$.
Note that for the $2d+1$ example, we will fail to prove $2d le textVCdim(H)$, therefore $textVCdim(H) neq 2d$, and consequently we must try to prove a smaller (probably harder to prove) upper bound $d+1$.
$endgroup$
Lets use the quotes from the book.
To give an upper bound, we need to prove that no set $S$ of cardinality
$d + 1$ can be shattered by $H$
By doing this, we are proving that $textVCdim(H) < d+1$, but it does not necessarily mean $textVCdim(H) = d$. For example, we may go for an easier-to-prove failure like sets with $2d+1$ members, and consequently prove $textVCdim(H) < 2d+1$, although we know $textVCdim(H) = d$. Therefore:
To give a lower bound $d$ for $textVCdim(H)$, it suffices to show that a set $S$
of cardinality $d$ can be shattered by $H$
We need to prove the lower bound as well to show that $d le textVCdim(H) < d+1$, which implies $textVCdim(H) = d$. The last equality means $H$ can shatter any set $S$ of size $1$ to $d$.
Note that for the $2d+1$ example, we will fail to prove $2d le textVCdim(H)$, therefore $textVCdim(H) neq 2d$, and consequently we must try to prove a smaller (probably harder to prove) upper bound $d+1$.
edited Apr 12 at 14:08
answered Apr 10 at 12:29
EsmailianEsmailian
4,114422
4,114422
$begingroup$
Thank you!! Now I understand I had some misunderstandings of the equations.
$endgroup$
– ML student
Apr 12 at 14:04
$begingroup$
@MLstudent My pleasure! Glad I could help.
$endgroup$
– Esmailian
Apr 12 at 14:10
add a comment |
$begingroup$
Thank you!! Now I understand I had some misunderstandings of the equations.
$endgroup$
– ML student
Apr 12 at 14:04
$begingroup$
@MLstudent My pleasure! Glad I could help.
$endgroup$
– Esmailian
Apr 12 at 14:10
$begingroup$
Thank you!! Now I understand I had some misunderstandings of the equations.
$endgroup$
– ML student
Apr 12 at 14:04
$begingroup$
Thank you!! Now I understand I had some misunderstandings of the equations.
$endgroup$
– ML student
Apr 12 at 14:04
$begingroup$
@MLstudent My pleasure! Glad I could help.
$endgroup$
– Esmailian
Apr 12 at 14:10
$begingroup$
@MLstudent My pleasure! Glad I could help.
$endgroup$
– Esmailian
Apr 12 at 14:10
add a comment |
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