Questions tagged [semi-supervised]

Semi-supervised learning refers to machine learning tasks using a mix of labeled and unlabeled data. The goal is to learn a mapping from inputs to outputs, or to obtain outputs for particular unlabeled inputs. The unlabeled data is used to learn about underlying structure of the inputs, which can improve learning about the relationship between inputs and outputs. Semi-supervised learning involves elements of both supervised and unsupervised learning.

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Active learning with a unlabelled pool - standard references & model-based labelling of the pool?

I'm looking into active learning for a multi-class classification problem, where there is a large pool of unlabelled data. I start out with a small set of labelled data and can labelled some more of ...
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What's the intitution behind contrastive learning or approach?

Maybe a noobs query, but recently I have seen a surge of papers w.r.t contrastive learning (a subset of semi-supervised learning). Two recent papers which I read, which detailed this approach are: ...
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Methods to enforce decision making of neural networks

Currently i have a well trained neural network. I plan to give the device containing the neural network to another person which then uses the neural network. That person gives me constant feedback ...
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24 views

Semi supervised learning with partially unobservable labels

As I understood the concept of semi-supervised learning is to train a classifier on the minimal available subset of correctly labeled data in order to predict the labels of a greater previously ...
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How to train a new classification model based on the labels obtained from the existing model? [duplicate]

We have a credit scoring model based on the logistic regression that we currently use in production. Every person who wants to get a short term loan is being scored by our current model and is either ...
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Can we say that RNN for time series is an example of semi-supervised learning?

I am learning neural nets, esp. focusing on RNN for my research problem. This question has nothing exactly to do with my research. With my understanding of RNN, I can think of it as an example of ...
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42 views

Does it make sense to use feature selection methods to reduce dimensionality for unsupervised clustering?

If I have a dataset that is labeled with positive and negative examples, and I'd like to cluster (i.e. unsupervised) only the positive examples, does it make sense to reduce dimensionality using ...
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Semi Supervised clustering

I have a dataset of points that belong to 10 different classes. These points are all unlabelled, besides one per class. In other words, I have a training set X of 10 points and a test set Y with N ...
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1answer
39 views

What is it called to cluster some inputs, then classify other inputs into those clusters?

I am learning about the problem of whole-book recognition, which is tangential to optical character recognition. Some of the strategies used to identify printed characters rely on first unsupervised ...
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How do you train a model on a dataset that's unlabeled but we know the percentage of every class?

Say we have a data set that's pictures of apples and oranges, but we don't know which is which. However the data is organized in such a way, that for some groups of images we know how many of them are ...
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Need some help understanding the factorised posterior in semi-supervised generative modelling

I am having a bit of trouble with the derivation in Kingma's semi-supervised generative modelling paper for the M2-model. The M2 model assumes a probabilistic model where the data $x$ is generated by ...
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Validity of PU learning when using character-level encoding and CNNs for text data

I'm trying to classify a large set of documents (~100M) as valid or invalid, based upon a small given set of labeled valid documents (~3k). I'd like to know if the PU learning approach described in ...
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Filling in Missing Data for Biological Experiment

I am trying to implement a semi-supervised learning model with biological data. In my case, I'm using features from DNA. I have a number of experiments each with many observations. Each observation ...
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In semi-supervies learning, is “low density separation” the same thing as “pseudo-labelling”?

I'm looking into the different methods of semi-supervised learning. In the wikipedia page, one of the methods described is called "low-density separation", where we attempt to minimize this loss ...
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Semi-supervised objective function VAE

In Kingma's paper on Semi-supervised learning https://arxiv.org/pdf/1406.5298.pdf, we are shown equations for the ELBO for the semisupervised case, however I am having a hard trying to derive the math ...
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65 views

Weak Supervision - training generative model without knowing the true label

Recently I've been reading about weak supervision. I understand most of the concept details, there's one thing that is not clear to me though. In the generative model part (creating generative model ...
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123 views

Is there something more effective than ladder networks for semi-supervised learning?

The paper Semi-Supervised Learning with Ladder Networks by Rasmus is famous and interesting but a bit old now. Did researchers find any better option for semi-supervised learning ? For example, what ...
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Co-training independent views

I found a tutorial on co-training that mentions that I need two views for training, which are conditionally independent. Could someone explain to me what this means regarding datasets?
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What derivative to use in Gradient boosting decision tree for a semi-supervised model

I am trying to build a semi-supervised prediction model with a Gradient Boosting decision trees. The learning phase is done using the following input: $X \in \mathbb{R}^{n\times p} $ $O(X) \in \...
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influence of oversampling on Semi-supervised multi-label learning

I have suggested a semi-supervised approach for the hierarchical multi-label classification task. I have included the MLSMOTE oversampling technique as a pre-processing step, and then evaluate the ...
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What is the convergence criteria of a semi-supervised learning algorithm

I would like to know about when to stop doing semi supervision? for example, I learn a classifier from a small dataset, and then I use it to label a pool of unlabelled dataset. How long should this ...
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61 views

Inference can be the goal of an unsupervised learning method or a semi-supervised learning method or even more of a reinforcement learning method?

I am new to machine learning, and I am reading a pair of machine learning books. These references talk about 2 different learning approaches: Prediction and inference, I understand the difference ...
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Differentiate Semi-supervised vs Transductive Learning?

Can someone explain the difference between transductive learning and semi-supervised learning? Or is semi-supervised learning a type of transductive learning? Transductive learning is when we do not ...
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Can we use result of cluster analysis (e.g. K-means) as the input to train a classifier?

I am having a project in which I need to group test cases failing due to same faults, and obviously, test cases are not labeled with due-to fault. So clearly we have an unsupervised classification (...
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207 views

How to perform cross validation in semi-supervised learning

Suppose in semi-supervised learning, we have labeled set $X_L$ and unlabeled set $X_U$ Is it ok to validate model performance on labeled data only? How to do cross-validation in transductive learning,...
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637 views

Understanding semi supervised technique called mean teachers

I am trying to understand applying semi supervised learning as described in this paper. Describing the final recipe as described in this paper: Take a supervised architecture and make a copy of it. ...
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76 views

When is it okay to label data yourself? (And semi-supervised learning)

i'm pretty new to machine learning so i think this might be a realy basic question. Let's imagine the following scenario: I want to classify projects as either active or inactive. Projects can be ...
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256 views

How does Google Photos face recognition use user-provided labels?

I'm working on a toy project which I think is analogous to the problem of detecting faces and assigning names to them in Google Photos, so I've been thinking about how that process might work. From a ...
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31 views

Weakly supervised learning and missing labels for data that likely contains that label

I would like to know how to deal with data that misses a label, but is likely to contain the label in a weakly supervised setting. Weakly supervised background Since labeling is a time consuming and ...
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215 views

How can I perform semi-supervised learning for Random forest algorithm in R language?

I want to train random forest by semi-supervised learning. And I have figured out a co-training framework for that, but I need to extract each tree and corresponding bootstrapped training dataset from ...
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152 views

Using partial measurements of output variable in modeling

My question is: How can we use partially measured output data in a training set? This is vague, so I concretize it in a whimsical tale. Squirrels Have Nuts, But How Many? Setup There is a set $S$ ...
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214 views

what's the difference between semi-supervised learning and partially supervised learning? [closed]

Isn't every semi-supervised problem also a partially supervised learning problem and vice versa?
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188 views

How to choose a method for binary classifier based on only positive and unlabelled examples?

I need to build a binary classifier with machine learning, as I fail to manually choose a combination of features to achieve minimal fraction of false positives. What is best practice for choosing a ...
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What exactly is semisupervised learning?

I have come across two descriptions of what semisupervised learning is, where one would have a small set $\mathcal{L}$ of labeled data and a larger set $\mathcal{U}$ of unlabeled data. The first ...
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Why does using pseudo-labeling non-trivially affect the results?

I've been looking into semi-supervised learning methods, and have come across the concept of "pseudo-labeling". As I understand it, with pseudo-labeling you have a set of labeled data as well as a ...
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416 views

Multi-class image segmentation with conflicting labels

I'm trying to perform multi-class semantic segmentation on a corpus made up of several sub-corpora. The difficult part is that across sub-corpora labels are not consistent. That is to say that similar ...
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1answer
111 views

Performing outlier detection before semi-supervised anomaly (novelty) detection

I want to perform semi-supervised anomaly (novelty) detection on data using machine learning methods (e.g. one class SVM). Is it sensible that in pre-processing step, I use outlier detection ...
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1answer
77 views

Semi-supervised parametric density estimation

I am trying to learn a (neural) density estimator for a set of data p(x), however I know that the true distribution is a mixture of two other distributions, q(x) and z(x), with fixed mixture weight. ...
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1answer
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What does the term “gold label” refer to in the context of semi-supervised classification?

Throughout the Snorkel tutorial here https://github.com/HazyResearch/snorkel and in the team's related white paper there's references to "gold labels", but the term evades definition. What are 'gold ...
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Can a neural net with unsupervised learning be used for detection of player formations in soccer?

I'm having a concrete problem I'm trying to solve but I'm not sure in which direction I should go. Goal: Identify formation of a soccer team based on a static positional data (x,y coordinates of ...
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32 views

Dimension reduction with semi-supervised embeddings

Is there a dimension reduction method (linear or non-linear) where the embeddings/projections of some of the input points are already known in advance and are taken into account during parameter ...
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1answer
40 views

A model to suggest salient examples to annotate

I have a very large dataset of items that are all initially unlabeled. A user picks at random 5-20 items, labels them and creates an initial training set for a model. The model is trained and is ready ...
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24 views

Feed guess features into unsupervised learning classification?

I've got an completely unlabeled dataset and my task is to classify it into positive and negative, two categories. As the data is unlabeled, I have to choose unsupervised classification; however, we ...
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161 views

Training models on biased samples

I have the following problem: The goal is, to find a model that classifies samples as risky, or less risky. However, only the risky samples are actually being manually investigated, i.e. labelled. ...
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303 views

Supervised, semi-supervised, or unsupervised? Confused

BACKGROUND: Consider the problem in cybersecruity that consists of classifying domain names as either malicious or legitimate based on various features such as the URL string, the name of the ...
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1answer
188 views

what are the top level subsets/domains of ML?

I'm not really happy with the mind maps I've been able to find on Google, most of them are algorithm based. I want to make a good one that is problem/solution domain based. Do I have this right for ...
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1answer
161 views

Performing Data Analysis on Results of ML classifier applied to Unlabelled Data

Suppose I have a large set of manually labelled data (e.g. 5000+ instances) with one of two lables, A or B, and I intend to build a ML classifier from this data. Using a proper methodology (e.g. ...
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101 views

Semi-supervised classification of documents

I know barely anything about semi-supervised learning, but I had the following idea. I classify documents in two classes, and would like to use the documents having the highest label confidences as ...
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54 views

Semi-supervised approach evaluation with few initially labeled observations

I'm using self-training as a semi-supervised approach to increase the size of the set of labeled observations. In each iteration a classifier is built based on already labeled observations and then ...
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3answers
558 views

Difference between semi-supervised learning and prediction?

What is the difference between semi-supervised learning and prediction? It seems to me they're the same (both are predicting the label)