What are some approaches for dealing with label noise with a known distribution?
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I'm pretty new to machine learning, and I am interested in some ideas for algorithms or references for papers. I am working on a problem where I have a labeled training data set in which each sample is associated with a real number $n_{0}, n_{1}, ..., n_{m} in mathbb{R} $. However, the actual values associated with the samples in the training data set that are labeled by $n_{i}$ are actually normally distributed about the value $n_{i}$ (e.g. the values of these samples are drawn from the distribution $n_{i}+mathcal{N}(mu,,sigma^{2})$ rather than all being exactly $n_{i}$, but because of the limitations of my data set I only know they are near $n_{i}$).
Ideally I would like to come up with a way to predict any value between $n_{0}$ and $n_{m}$ for new samples that I would test (not just the discrete values represented by the $m+1$ labels in my data set). What would be the best way to approach this kind of problem?
machine-learning multilabel-classification labels
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KDLKDL
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I'm pretty new to machine learning, and I am interested in some ideas for algorithms or references for papers. I am working on a problem where I have a labeled training data set in which each sample is associated with a real number $n_{0}, n_{1}, ..., n_{m} in mathbb{R} $. However, the actual values associated with the samples in the training data set that are labeled by $n_{i}$ are actually normally distributed about the value $n_{i}$ (e.g. the values of these samples are drawn from the distribution $n_{i}+mathcal{N}(mu,,sigma^{2})$ rather than all being exactly $n_{i}$, but because of the limitations of my data set I only know they are near $n_{i}$).
Ideally I would like to come up with a way to predict any value between $n_{0}$ and $n_{m}$ for new samples that I would test (not just the discrete values represented by the $m+1$ labels in my data set). What would be the best way to approach this kind of problem?
machine-learning multilabel-classification labels
asked 1 min ago
KDLKDL
1
New contributor
KDL is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
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add a comment |
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I'm pretty new to machine learning, and I am interested in some ideas for algorithms or references for papers. I am working on a problem where I have a labeled training data set in which each sample is associated with a real number $n_{0}, n_{1}, ..., n_{m} in mathbb{R} $. However, the actual values associated with the samples in the training data set that are labeled by $n_{i}$ are actually normally distributed about the value $n_{i}$ (e.g. the values of these samples are drawn from the distribution $n_{i}+mathcal{N}(mu,,sigma^{2})$ rather than all being exactly $n_{i}$, but because of the limitations of my data set I only know they are near $n_{i}$).
Ideally I would like to come up with a way to predict any value between $n_{0}$ and $n_{m}$ for new samples that I would test (not just the discrete values represented by the $m+1$ labels in my data set). What would be the best way to approach this kind of problem?
machine-learning multilabel-classification labels
asked 1 min ago
KDLKDL
1
New contributor
KDL is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
$endgroup$
I'm pretty new to machine learning, and I am interested in some ideas for algorithms or references for papers. I am working on a problem where I have a labeled training data set in which each sample is associated with a real number $n_{0}, n_{1}, ..., n_{m} in mathbb{R} $. However, the actual values associated with the samples in the training data set that are labeled by $n_{i}$ are actually normally distributed about the value $n_{i}$ (e.g. the values of these samples are drawn from the distribution $n_{i}+mathcal{N}(mu,,sigma^{2})$ rather than all being exactly $n_{i}$, but because of the limitations of my data set I only know they are near $n_{i}$).
Ideally I would like to come up with a way to predict any value between $n_{0}$ and $n_{m}$ for new samples that I would test (not just the discrete values represented by the $m+1$ labels in my data set). What would be the best way to approach this kind of problem?
machine-learning multilabel-classification labels
machine-learning multilabel-classification labels
asked 1 min ago
KDLKDL
1
New contributor
KDL is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
asked 1 min ago
KDLKDL
1
New contributor
KDL is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
asked 1 min ago
KDLKDL
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New contributor
KDL is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
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asked 1 min ago
KDLKDL
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asked 1 min ago
KDLKDL
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1
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KDL is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
New contributor
KDL is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
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