Are there known techniques to transform features X classified as C to features Y classified as C'r - rpart in text mining for classification of documentsNeural network, Support Vector Machine or something else to classify into 7 groupsDefinition - center of the cluster with non-Euclidean distanceDatamodel for cluster analysis terms & segmentationWhat could be possible features of a textual word?Explain output of a given classifier w.r.t featuresRecommending college degrees based on high school subjectsIs my model overfitting when I add new features?Audio classification data balanceWhat's the right way to setup an image classifier by multiple params?
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Are there known techniques to transform features X classified as C to features Y classified as C'
r - rpart in text mining for classification of documentsNeural network, Support Vector Machine or something else to classify into 7 groupsDefinition - center of the cluster with non-Euclidean distanceDatamodel for cluster analysis terms & segmentationWhat could be possible features of a textual word?Explain output of a given classifier w.r.t featuresRecommending college degrees based on high school subjectsIs my model overfitting when I add new features?Audio classification data balanceWhat's the right way to setup an image classifier by multiple params?
$begingroup$
I don't think the wording of my question is that clear myself, but I don't have any better words suitable for a title (on top of my head at least). I was wondering if given features X that is classified by a model M as class C, is there a way to find the features Y that is relatively "close" to X so that it would be classified as class C' by M.
I was thinking if some sort of clustering can help such as k-means and then getting the centroid of class C' and using that. The final idea is to get the difference between X and Y to be displayed. Does that sound reasonable? I'm not really a data scientist so want to check up on my thoughts.
If someone can suggest a paper or direction that would be much appreciated
EDIT:
For clarification. The purpose of this is that I have a set of people's skills and their jobs and I want to be able to give an advice of what skills a person needs to cultivate for their desired job.
E.g I can program, have a CS degree, experienced with unix etc. and am classified as software developer (skills are codified into numerical values not text anymore) and I want to work as a chemical engineer. I want to know the skills I would need so that I can be classified as fit to be a chemical engineer.
So X is my set of skills, C is software developer, C' is chemical engineer, and Y is the set of skills appropriate for a chemical engineer that I am looking for.
classification clustering
asked Feb 13 at 7:46
Btara TruhandarienBtara Truhandarien
12
$endgroup$
add a comment |
$begingroup$
I don't think the wording of my question is that clear myself, but I don't have any better words suitable for a title (on top of my head at least). I was wondering if given features X that is classified by a model M as class C, is there a way to find the features Y that is relatively "close" to X so that it would be classified as class C' by M.
I was thinking if some sort of clustering can help such as k-means and then getting the centroid of class C' and using that. The final idea is to get the difference between X and Y to be displayed. Does that sound reasonable? I'm not really a data scientist so want to check up on my thoughts.
If someone can suggest a paper or direction that would be much appreciated
EDIT:
For clarification. The purpose of this is that I have a set of people's skills and their jobs and I want to be able to give an advice of what skills a person needs to cultivate for their desired job.
E.g I can program, have a CS degree, experienced with unix etc. and am classified as software developer (skills are codified into numerical values not text anymore) and I want to work as a chemical engineer. I want to know the skills I would need so that I can be classified as fit to be a chemical engineer.
So X is my set of skills, C is software developer, C' is chemical engineer, and Y is the set of skills appropriate for a chemical engineer that I am looking for.
classification clustering
asked Feb 13 at 7:46
Btara TruhandarienBtara Truhandarien
12
$endgroup$
$begingroup$
If this example is correct add a similar one for clarity: I have a set of profile (side) pictures of blue cars classified as 'car', I want a set of profile pictures of blue motorcycles classified as 'motorcycle'. Two sets of images (pixels as features) are close since they are both vehicles, blue, and photographed from the side.
$endgroup$
– Esmailian
yesterday
$begingroup$
@Esmailian I added some more detail of what I am trying to do. Thanks!
$endgroup$
– Btara Truhandarien
yesterday
$begingroup$
For this task you first need a sizable data set of (skills, job title) for many individuals to begin with, specially chemical engineers. Your personal information is not enough. Then, a fast approach would be to find a set of chemical engineers that the distance of their features to yours is the smallest. This is a good start.
$endgroup$
– Esmailian
yesterday
add a comment |
$begingroup$
I don't think the wording of my question is that clear myself, but I don't have any better words suitable for a title (on top of my head at least). I was wondering if given features X that is classified by a model M as class C, is there a way to find the features Y that is relatively "close" to X so that it would be classified as class C' by M.
I was thinking if some sort of clustering can help such as k-means and then getting the centroid of class C' and using that. The final idea is to get the difference between X and Y to be displayed. Does that sound reasonable? I'm not really a data scientist so want to check up on my thoughts.
If someone can suggest a paper or direction that would be much appreciated
EDIT:
For clarification. The purpose of this is that I have a set of people's skills and their jobs and I want to be able to give an advice of what skills a person needs to cultivate for their desired job.
E.g I can program, have a CS degree, experienced with unix etc. and am classified as software developer (skills are codified into numerical values not text anymore) and I want to work as a chemical engineer. I want to know the skills I would need so that I can be classified as fit to be a chemical engineer.
So X is my set of skills, C is software developer, C' is chemical engineer, and Y is the set of skills appropriate for a chemical engineer that I am looking for.
classification clustering
asked Feb 13 at 7:46
Btara TruhandarienBtara Truhandarien
12
$endgroup$
I don't think the wording of my question is that clear myself, but I don't have any better words suitable for a title (on top of my head at least). I was wondering if given features X that is classified by a model M as class C, is there a way to find the features Y that is relatively "close" to X so that it would be classified as class C' by M.
I was thinking if some sort of clustering can help such as k-means and then getting the centroid of class C' and using that. The final idea is to get the difference between X and Y to be displayed. Does that sound reasonable? I'm not really a data scientist so want to check up on my thoughts.
If someone can suggest a paper or direction that would be much appreciated
EDIT:
For clarification. The purpose of this is that I have a set of people's skills and their jobs and I want to be able to give an advice of what skills a person needs to cultivate for their desired job.
E.g I can program, have a CS degree, experienced with unix etc. and am classified as software developer (skills are codified into numerical values not text anymore) and I want to work as a chemical engineer. I want to know the skills I would need so that I can be classified as fit to be a chemical engineer.
So X is my set of skills, C is software developer, C' is chemical engineer, and Y is the set of skills appropriate for a chemical engineer that I am looking for.
classification clustering
classification clustering
asked Feb 13 at 7:46
Btara TruhandarienBtara Truhandarien
12
asked Feb 13 at 7:46
Btara TruhandarienBtara Truhandarien
12
edited yesterday
Btara Truhandarien
asked Feb 13 at 7:46
Btara TruhandarienBtara Truhandarien
12
asked Feb 13 at 7:46
Btara TruhandarienBtara Truhandarien
12
asked Feb 13 at 7:46
Btara TruhandarienBtara Truhandarien
12
12
$begingroup$
If this example is correct add a similar one for clarity: I have a set of profile (side) pictures of blue cars classified as 'car', I want a set of profile pictures of blue motorcycles classified as 'motorcycle'. Two sets of images (pixels as features) are close since they are both vehicles, blue, and photographed from the side.
$endgroup$
– Esmailian
yesterday
$begingroup$
@Esmailian I added some more detail of what I am trying to do. Thanks!
$endgroup$
– Btara Truhandarien
yesterday
$begingroup$
For this task you first need a sizable data set of (skills, job title) for many individuals to begin with, specially chemical engineers. Your personal information is not enough. Then, a fast approach would be to find a set of chemical engineers that the distance of their features to yours is the smallest. This is a good start.
$endgroup$
– Esmailian
yesterday
add a comment |
$begingroup$
If this example is correct add a similar one for clarity: I have a set of profile (side) pictures of blue cars classified as 'car', I want a set of profile pictures of blue motorcycles classified as 'motorcycle'. Two sets of images (pixels as features) are close since they are both vehicles, blue, and photographed from the side.
$endgroup$
– Esmailian
yesterday
$begingroup$
@Esmailian I added some more detail of what I am trying to do. Thanks!
$endgroup$
– Btara Truhandarien
yesterday
$begingroup$
For this task you first need a sizable data set of (skills, job title) for many individuals to begin with, specially chemical engineers. Your personal information is not enough. Then, a fast approach would be to find a set of chemical engineers that the distance of their features to yours is the smallest. This is a good start.
$endgroup$
– Esmailian
yesterday
$begingroup$
If this example is correct add a similar one for clarity: I have a set of profile (side) pictures of blue cars classified as 'car', I want a set of profile pictures of blue motorcycles classified as 'motorcycle'. Two sets of images (pixels as features) are close since they are both vehicles, blue, and photographed from the side.
$endgroup$
– Esmailian
yesterday
$begingroup$
If this example is correct add a similar one for clarity: I have a set of profile (side) pictures of blue cars classified as 'car', I want a set of profile pictures of blue motorcycles classified as 'motorcycle'. Two sets of images (pixels as features) are close since they are both vehicles, blue, and photographed from the side.
$endgroup$
– Esmailian
yesterday
$begingroup$
@Esmailian I added some more detail of what I am trying to do. Thanks!
$endgroup$
– Btara Truhandarien
yesterday
$begingroup$
@Esmailian I added some more detail of what I am trying to do. Thanks!
$endgroup$
– Btara Truhandarien
yesterday
$begingroup$
For this task you first need a sizable data set of (skills, job title) for many individuals to begin with, specially chemical engineers. Your personal information is not enough. Then, a fast approach would be to find a set of chemical engineers that the distance of their features to yours is the smallest. This is a good start.
$endgroup$
– Esmailian
yesterday
$begingroup$
For this task you first need a sizable data set of (skills, job title) for many individuals to begin with, specially chemical engineers. Your personal information is not enough. Then, a fast approach would be to find a set of chemical engineers that the distance of their features to yours is the smallest. This is a good start.
$endgroup$
– Esmailian
yesterday
add a comment |
1 Answer
1
active
oldest
votes
$begingroup$
As far as i can understand , you are interested in Adversarial training techniques.
check this out:
https://www.deeplearningbook.org/contents/regularization.html @7.13 Adversarial Training and the image example of the panda.
Although the concept is opposite, i.e it finds out points near and similar to the existing classified regions which the model classifies wrong but sometimes the points are close to be identical like: the panda example in the reference at page 265.
answered Feb 13 at 9:12
NIKESH SINGHNIKESH SINGH
11
$endgroup$
$begingroup$
do you know anything that is closer to what I was describing?
$endgroup$
– Btara Truhandarien
Feb 19 at 5:12
add a comment |
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1 Answer
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$begingroup$
As far as i can understand , you are interested in Adversarial training techniques.
check this out:
https://www.deeplearningbook.org/contents/regularization.html @7.13 Adversarial Training and the image example of the panda.
Although the concept is opposite, i.e it finds out points near and similar to the existing classified regions which the model classifies wrong but sometimes the points are close to be identical like: the panda example in the reference at page 265.
answered Feb 13 at 9:12
NIKESH SINGHNIKESH SINGH
11
$endgroup$
$begingroup$
do you know anything that is closer to what I was describing?
$endgroup$
– Btara Truhandarien
Feb 19 at 5:12
add a comment |
$begingroup$
As far as i can understand , you are interested in Adversarial training techniques.
check this out:
https://www.deeplearningbook.org/contents/regularization.html @7.13 Adversarial Training and the image example of the panda.
Although the concept is opposite, i.e it finds out points near and similar to the existing classified regions which the model classifies wrong but sometimes the points are close to be identical like: the panda example in the reference at page 265.
answered Feb 13 at 9:12
NIKESH SINGHNIKESH SINGH
11
$endgroup$
$begingroup$
do you know anything that is closer to what I was describing?
$endgroup$
– Btara Truhandarien
Feb 19 at 5:12
add a comment |
$begingroup$
As far as i can understand , you are interested in Adversarial training techniques.
check this out:
https://www.deeplearningbook.org/contents/regularization.html @7.13 Adversarial Training and the image example of the panda.
Although the concept is opposite, i.e it finds out points near and similar to the existing classified regions which the model classifies wrong but sometimes the points are close to be identical like: the panda example in the reference at page 265.
answered Feb 13 at 9:12
NIKESH SINGHNIKESH SINGH
11
$endgroup$
As far as i can understand , you are interested in Adversarial training techniques.
check this out:
https://www.deeplearningbook.org/contents/regularization.html @7.13 Adversarial Training and the image example of the panda.
Although the concept is opposite, i.e it finds out points near and similar to the existing classified regions which the model classifies wrong but sometimes the points are close to be identical like: the panda example in the reference at page 265.
answered Feb 13 at 9:12
NIKESH SINGHNIKESH SINGH
11
answered Feb 13 at 9:12
NIKESH SINGHNIKESH SINGH
11
answered Feb 13 at 9:12
NIKESH SINGHNIKESH SINGH
11
answered Feb 13 at 9:12
NIKESH SINGHNIKESH SINGH
11
11
$begingroup$
do you know anything that is closer to what I was describing?
$endgroup$
– Btara Truhandarien
Feb 19 at 5:12
add a comment |
$begingroup$
do you know anything that is closer to what I was describing?
$endgroup$
– Btara Truhandarien
Feb 19 at 5:12
$begingroup$
do you know anything that is closer to what I was describing?
$endgroup$
– Btara Truhandarien
Feb 19 at 5:12
$begingroup$
do you know anything that is closer to what I was describing?
$endgroup$
– Btara Truhandarien
Feb 19 at 5:12
add a comment |
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$begingroup$
If this example is correct add a similar one for clarity: I have a set of profile (side) pictures of blue cars classified as 'car', I want a set of profile pictures of blue motorcycles classified as 'motorcycle'. Two sets of images (pixels as features) are close since they are both vehicles, blue, and photographed from the side.
$endgroup$
– Esmailian
yesterday
$begingroup$
@Esmailian I added some more detail of what I am trying to do. Thanks!
$endgroup$
– Btara Truhandarien
yesterday
$begingroup$
For this task you first need a sizable data set of (skills, job title) for many individuals to begin with, specially chemical engineers. Your personal information is not enough. Then, a fast approach would be to find a set of chemical engineers that the distance of their features to yours is the smallest. This is a good start.
$endgroup$
– Esmailian
yesterday