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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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Is Oversampling beneficial in semi-supervised 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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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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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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What is the effect of noisy labels in distant-supervision?

I am just learning about distant supervision. I read the paper of Mintz et al. and trying to get some intuition of how the noise influences the classification. My general assumption is, that having ...
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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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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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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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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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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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Factor Graphs for Distant Supervision

Are factor graphs the state-of-the-art for relation classification with distant supervision? It seems to be the original way (Riedel et al 2010) of dealing with wrong relation mentions. Is it still ...
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How to assign labels to un-labeled documents

I have a bunch of text documents and they are not labeled but each document represents one or more than one category/label. I'd like to assign the appropriate label (s) to each document. What's the ...
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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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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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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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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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Semisupervised and Multiclass Classification

I have a dataset that includes around 400 instances (400 users' instances) with 10 features. As follows: ...
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(Re)-Train on a small dataset and new incoming data

I would like to train a classifier (doesn't matter which learning algorithm) on a small set of training data. As soon as the system predicts new samples, it should collect them, add the samples to the ...
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Why should each layer's child network output be close to parent network's output for variance regularizer?

I am reading up on PEA (Pseudo ensemble agreement) regularizer. specificaly in the neural networks domain. It introduces the concept of perturbing the model a little and forcing the model to make ...
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Graph-Based Semi-Supervised Learning for NLP Text Classification

I have hundreds of various job titles, where some of the titles may sound different but ultimately should be classified to be the same position (ex. Call Center, Call Center, Customer Contact Center ...
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Machine Learning Algorithm for Dynamic Environments

Which methods are best for managing and predicting and labeling data in a dynamic environment? The system data distribution changes and it is not static. The system can have different normal settings ...
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281 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
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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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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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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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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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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
118 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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85 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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1answer
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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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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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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)
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235 views

Is semi-supervised regression useful?

I understand why semi-supervised classification, but don't know why semi-supervised regression is. https://en.wikipedia.org/wiki/Semi-supervised_learning#/media/File:...
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2answers
986 views

Constrained Clustering Implementation R or Python? [closed]

Can anyone point me to an implementation in R or Python of a constrained clustering algorithm? In case this is overly broad, I am hoping to exploit known must-link/cannot-link pairs to improve the ...
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Supervised learning for audio files with uncertain labels

There exists a collection of audio files, each 30 seconds long. Such an audio file contains the recording of the manufacturing of a certain small piece in a machine. After manufacturing the piece, it ...
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1answer
788 views

Classification using lookup table

I have a matrix of samples to classify, samples are matrix columns and features (noisy or estimated features) are matrix rows. On the other hand, I have a lookup table for correspondence between ...
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118 views

Performance measure for iterative semi-supervised learning

Consider the problem of semi-supervised learning where, in each round, the labels of all data points are guessed and then the label of a random data point is revealed. As the labels of more and more ...
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Is this an example of semi-supervised learning?

I am working with advertising data, specifically click-throughs as a measure of engagement. Each row of my data set represents a user that received an ad impression. The label would be whether the ...
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1answer
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Is it possible to train a model when I have just one class labbeled?

I have a large dataset (~1,700,000) which I would like to binary classify. I also have a not that small sample (~8,000) classified as one of those classes (let's say TRUE condition), but I have none (...
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1answer
773 views

combining supervised and unsupervised learning

I am trying to classify short natural language documents, for which I have a small labeled dataset. Using out-of-the-box document classifiers and basic td-idf representation, I am able to get "...
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1answer
54 views

Training classification/clustering with regression data

I have a problem with continuous feature and outcome data. The features are weak predictors. I'd like to be able to cluster my features into $k$ classes. This is not semi-supervised learning so much ...
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1answer
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How are performance measures affected in PU learning?

When learning from only positive and unlabelled data (PU learning), how are performance measures affected, when compared to a standard supervised setting? For simplicity, let's assume that the entire ...
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Best practices to evaluate semi-supervised learning methods

Some background I have been working on multiclass classification method. I have an idea on how to extend this method such that it can be semi-supervised. What are the best practices in evaluating ...
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1answer
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Use clustering to create labels of unlabeled data and then classify a test set (available or not in the clustering)?

Let's say that I use Dynamic Time Warping (DTW) along with K-Medoids to cluster unlabeled time-series into a number of clusters. In this way, several clustering solutions in $k_i,i=[1,...,m]$ clusters ...
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Interpreting Algorithm for Semi-supervised Multiclass Adaboost

I'm trying to implement a version of the algorithm described in this paper but I'm a little unsure of how the loss function is actually utilized. It defines its loss function as $L(Y^l,Y^u,H) = \...
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1answer
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Why is (deep) unsupervised and semi-supervised learning so hard?

I recently read a paper quoting: General unsupervised learning is a long-standing conceptual problem in machine learning. Supervised learning is successful because it can be solved by the ...