ML algorithms for regression in the case of 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 for using a ML algorithm for regression when the labeled data has label noise with a known distribution.
I am working on a problem where I have raw time-series data from a scientific instrument, and I'd like to associate each measurement with a scalar value that represents a physical parameter associated with that sample. I am starting with a labeled training data set in which each sample is associated with a discrete number $n_{0}, n_{1}, ..., n_{m} in mathbb{R} $. However, the actual scalar values for the physical parameter associated with the samples in the training data set labeled by $n_{i}$ are actually normally distributed about the value $n_{i}$ (e.g. the scalar values of these samples are drawn from the distribution $n_{i}+mathcal{N}(mu_{i},,sigma_{i}^{2})$ rather than all being exactly $n_{i}$ but because of the limitations of my experimental data I only know they are near $n_{i}$). I also know the values of $mu_{i}$ and $sigma_{i}$ for each of my $m+1$ labels.
Ideally I would like to come up with a way to predict any scalar 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 training data set that I've measured). What would be the best way to approach this kind of problem?
machine-learning regression 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 for using a ML algorithm for regression when the labeled data has label noise with a known distribution.
I am working on a problem where I have raw time-series data from a scientific instrument, and I'd like to associate each measurement with a scalar value that represents a physical parameter associated with that sample. I am starting with a labeled training data set in which each sample is associated with a discrete number $n_{0}, n_{1}, ..., n_{m} in mathbb{R} $. However, the actual scalar values for the physical parameter associated with the samples in the training data set labeled by $n_{i}$ are actually normally distributed about the value $n_{i}$ (e.g. the scalar values of these samples are drawn from the distribution $n_{i}+mathcal{N}(mu_{i},,sigma_{i}^{2})$ rather than all being exactly $n_{i}$ but because of the limitations of my experimental data I only know they are near $n_{i}$). I also know the values of $mu_{i}$ and $sigma_{i}$ for each of my $m+1$ labels.
Ideally I would like to come up with a way to predict any scalar 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 training data set that I've measured). What would be the best way to approach this kind of problem?
machine-learning regression multilabel-classification labels
asked 53 mins ago
KDLKDL
11
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 |
$begingroup$
I'm pretty new to machine learning, and I am interested in some ideas for algorithms or references for papers for using a ML algorithm for regression when the labeled data has label noise with a known distribution.
I am working on a problem where I have raw time-series data from a scientific instrument, and I'd like to associate each measurement with a scalar value that represents a physical parameter associated with that sample. I am starting with a labeled training data set in which each sample is associated with a discrete number $n_{0}, n_{1}, ..., n_{m} in mathbb{R} $. However, the actual scalar values for the physical parameter associated with the samples in the training data set labeled by $n_{i}$ are actually normally distributed about the value $n_{i}$ (e.g. the scalar values of these samples are drawn from the distribution $n_{i}+mathcal{N}(mu_{i},,sigma_{i}^{2})$ rather than all being exactly $n_{i}$ but because of the limitations of my experimental data I only know they are near $n_{i}$). I also know the values of $mu_{i}$ and $sigma_{i}$ for each of my $m+1$ labels.
Ideally I would like to come up with a way to predict any scalar 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 training data set that I've measured). What would be the best way to approach this kind of problem?
machine-learning regression multilabel-classification labels
asked 53 mins ago
KDLKDL
11
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 for using a ML algorithm for regression when the labeled data has label noise with a known distribution.
I am working on a problem where I have raw time-series data from a scientific instrument, and I'd like to associate each measurement with a scalar value that represents a physical parameter associated with that sample. I am starting with a labeled training data set in which each sample is associated with a discrete number $n_{0}, n_{1}, ..., n_{m} in mathbb{R} $. However, the actual scalar values for the physical parameter associated with the samples in the training data set labeled by $n_{i}$ are actually normally distributed about the value $n_{i}$ (e.g. the scalar values of these samples are drawn from the distribution $n_{i}+mathcal{N}(mu_{i},,sigma_{i}^{2})$ rather than all being exactly $n_{i}$ but because of the limitations of my experimental data I only know they are near $n_{i}$). I also know the values of $mu_{i}$ and $sigma_{i}$ for each of my $m+1$ labels.
Ideally I would like to come up with a way to predict any scalar 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 training data set that I've measured). What would be the best way to approach this kind of problem?
machine-learning regression multilabel-classification labels
machine-learning regression multilabel-classification labels
asked 53 mins ago
KDLKDL
11
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 53 mins ago
KDLKDL
11
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.
edited 2 mins ago
KDL
asked 53 mins ago
KDLKDL
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asked 53 mins ago
KDLKDL
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asked 53 mins ago
KDLKDL
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11
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