Questions tagged [semi-supervised-learning]

Making use of both unsupervised and supervised learning paradigms to train on a partially labelled dataset.

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what about differences between the meta and semi-supervised and self-supervised and active and federated and few-shot learning?

what about difference between the meta learning and semi-supervised learning and self-supervised learning and active learning and federated learning and few-shot learning? in application and in ...
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Comparison of zero-shot learning, one-shot learning, and few-shot learning?

What are the differences between zero-shot , one-shot , few-shot learning? and what about their difference in usage/ application? fields of their application? Comparisons of their Pros & Cons?
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scikit learn Label propagation

Label propagation provided by scikit-learn only allows two options for constructing the affinity matrix. 1) RBF and 2) kNN. The former results in a completed graph where weight on each edges is the ...
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What are the benefits of combining semi-supervised and supervised learning methods?

I've been looking into semi-supervised learning more, specifically label propagation and label spreading. When reading through tutorials and some papers I've seen it mentioned that often times the ...
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forcasting anomaly in products

I have a question about the forecasting of anomalies. I would be very grateful if you could refer me to some papers that deal with this kind of problem or give me some hints to start with this problem....
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Semi supervised learning on graphs

I have the following semi-supervised problem: ...
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Need some guidance in implementing semi-supervised learning for Video Summarization

I'm new to deep learning and I wish to implement a semi-supervised algorithm for video summarization. I am using the "Lamem" dataset and I have frames from the video along with the ...
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Given a set of options where one option is selected prior to an outcome, how to model optimal selection that will increase likelihood of (+) outcome

Say that we have a set of treatment plans (the options) available to a patient. Treatment plans can be invasive-surgery, no-surgery, less-invasive surgery ext... We have a dataset where a treatment ...
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Semi-supervised classification with SelfTrainingClassifier: no training after calling fit()

I am practicing semi-supervised learning, at the moment experimenting with sklearn.semi_supervised.SelfTrainingClassifier. I found a dataset for multiclass ...
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Semi-supervised learning algorithms for multibox/priorbox detection in images

I've read lots of papers on query strategies like BADGE, SCALAR, BatchBALD etc, but they all seem to be for situations where there is a single label to give an image (is this a cat, dog or horse), but ...
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Do PNU (Positive-Negative-Unlabelled) methods expand to non-binary classification

Looking at various materials for PNU Semi-Supervised Learning, they seem to be all based around binary classification, as the name implies. How easy is to apply these methods to classifications with ...
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How Pretraining part actually work in Wav2vec models? Which data is qualify to be the adequat for fine-tuning part the model of speech2text

Pretraining and fine-tuning the algorithm of wav2vec2.0, the new one using in FAcebookAI to do speech to text for low-resource language. I didn't actually get how the model does the pretraining part ...
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What the differences between self-supervised/semi-supervised in NLP?

GPT-1 mentions both Semi-supervised learning and Unsupervised pre-training but it seems like the same to me. Moreoever, "Semi-supervised Sequence Learning" of Dai and Le also more like self-...
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Label spreading for classification/clustering problems

I have a question regarding label propagation and label spreading semi-supervised algorithms. I am working on building a look-alike model to identify marketing personas. Using supervised learning ...
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Extract key phrases for binary outcome

I have a set of phrases that lead to a binary outcome (accept/reject) and I was wondering what techniques are most helpful for extracting key phrases that are most likely to determine the outcome, ...
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Pros and Cons of Positive Unlabeled learning?

I've been looking for papers that discuss the pros and cons of positive unlabeled learning but I haven't been able to find anything. I'm looking to compare the general differences between creating a ...
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Solutions for Labelling Training Data for Binary Classification Problems

I have a huge dataset for which I am trying to use an 80-20 (Holdout method) approach to train and test my model. However, the dataset I have been given has 6m rows. The objective is to train+test+...
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Is it better to have one model with more categories or less with two for multi-label classification?

For classifying text into three classes question, complain and complements where each sample can have multi-labels (question and complains, question and complements): is it better to have one model ...
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How to approach semi-supervised binary classification problem with few labels only from one class?

I confront with a binary classification problem where I do have a few instances with labels (so far this is "semi-supervised" learning as far as I know), but only from the positive class. So ...
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Identifying templates from SMS text

I am building an app where I identify information from the SMS, something similar to expense management apps. I have a parser which reads all the SMS of user, identifies SMS of interest and parses ...
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How do I build a model to improve CTR on campaign?

I am trying to build a propensity model for a client to increase the CTR. Client has the list of people who clicked in the previous campaigns but doesn't have the data on the list of people who didn't ...
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Semi-supervised anomaly detection

I am currently exploring anomaly detection methods for my work and, basically I have gone through Local Oulier Factor and Isolation Forests, both unsupervised methods. Now, the thing is, there might ...
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What is the difference between all the different types of learning within machine learning?

This is a question that is really hard to google, and the differences are confusing. Does anyone have good examples of the differences between them all? Supervised Learning Semi-Supervised Learning ...
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Validity of PU learning while using character-level encoding using CNNs for classifying 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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4 answers
485 views

Supervised clustering

I'm working on a clustering problem. I have a training set composed of sets of points where the clusters are known and I want to find the good clusters on a testing dataset. It's a kind of supervised ...
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Inductive vs Transductive Learning

I am reading about Inductive and Transductive Learning. Some of the questions that come to mind are the following: What is the difference between these two? Which algorithms are usually employed for ...
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using dataset to classifying and labelling another unlabeded dataset

I collect a collection of posts from Facebook and I use a published sentiment datset to labeling my collected dataset. is this a right technique and what its name is this transfer-learning ?
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Is there an algorithm for categorizing unlabeled samples into K classes? [closed]

I am not sure if this would be considered unsupervised, or semi-supervised learning. I am looking for an algorithm that will take N input arrays of features, and then cluster samples(not features) ...
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Ignoring unlabeled data for a single class

I have a data set of transactions with a binary flag labeling each as fraud or not fraud. However, it can take up to 90 days for a transaction to reveal itself as fraudulent. Sometimes it happens in a ...
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What kind of learning is needed for anomaly detection? Supervised learning, semi-supervised learning or unsupervised learning?

I am doing anomaly detection recently, one of the methods is using AEs model to learn the pattern of normal samples. Determine it as an abnormal sample if it doesn’t match the pattern of normal ...
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1 answer
235 views

What is the convergence criteria of a semi-supervised learning algorithm?

I would like to know when to stop doing semi supervision? For example, if I learn a classifier from a small dataset and then use it to label a pool of unlabelled dataset. I then use the newly labelled ...
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Accuracy after selftraining didn't change

I used Decisiton Tree Classifier which I trained with 50 000 samples. I have also set with unlabeled samples, so I decided to use self training algorithm. Unlabeled set has 10 000 samples. I would ...
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How to get probability of classification

I have the binary classification, I tried several models KNN, SVM, decision tree, and random forest. I have 50 000 samples, X_train has 50 000 rows and 2300 columns....
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General way of constructing adjacency matrix in Laplacian SVM semi-supervised technique

I am trying to implement a Laplacian SVM classifier (trained in primal) using algorithm from this paper. I would like to know what is the most common way of constructing adjacency matrix and the most ...
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Neural Network for detecting/checking for requirements in diagrams

My question is more about what approach is a good/the best approach for my problem: THE PROBLEM - I'm an (mechanical/software) engineer and we take extensive amount of time to review technical ...
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Improving automated ingestion system using Machine Learning and/or NLP

I'm working on a automated ingestion system which takes a PDF or doc file or a URL. It then parses the file and get me the required text in a json format but there are some error and there are few ...
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Adapting Neural Network to new domain without labels

Is there an approach for the following problem: Lets say, I trained a neural network on a big dataset for categorizing different fruits in $k$ classes. Afterwards I got a nice model, which performs ...
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1 answer
306 views

Semi-supervised learning for regression

It is mentioned on this page that Label Propagation of scikit-learn can be used for regression also. However, nothing is ...
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how to build a predictive model without training data neither historical data

I m trying to score "how much a product is expected in the market". I created some features: How much this product is used each year. Where was it used . how many product for each country. the main ...
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Semi Supervised Learning without label propagation

I am trying to cluster some words by affinity. Using Word2Vec I obtained vector representation of every word that I can cluster with a normal unsupervised method. Of these words, though, I know the ...
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3 votes
2 answers
379 views

Time series binary classificaiton with labelling issues

My situation is quite complicated so I will give a similar example from a simpler domain. Suppose we want to try to predict WHEN a mobile game users will make a purchase if given a sale. Almost every ...
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1 answer
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suggest ingredients based on recipe title [closed]

I would like to construct system that would suggest user ingredients once he/she inputs title of the recipe. I think that this is the task of machine learning or AI, but on the other hand I am pretty ...
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1 vote
1 answer
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Problem faced when collect data randomly from cluster [closed]

I have a semi structured data set. I need to collect some data (unlabeled) randomly for labeling. As initiative at first I separated labeled and unlabeled data. Then I convert those data from string ...
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How to do semi-supervised learning for regression

I am doing regression analysis on a data set with over 20000 samples using scikit learn. Trying to use regression models to fit three features to label which is a score ranges from 0 to 10. Problem is ...
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Can You Purposely Bias A Clustering Model?

We have a large amount (Billions) of high cardinality, mixed nominal & numerical data, and are performing some clustering on it as an experiment. There is a small subset of these data, however, ...
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5 votes
1 answer
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Custom conditional loss function in Keras

I'm looking for a way to create a conditional loss function that looks like this: there is a vector of labels, say l (l has the same length as the input x), then for a given input (y_true, y_pred, l) ...
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Why positive-unlabeled learning?

Machine learning can be divided into several areas: supervised learning, unsupervised learning, semi-supervised learning, learning to rank, recommendation systems, etc, etc. One such area is PU ...
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1 answer
441 views

How should I construct a binary classifier for thousands of positive data and millions of unlabeled data?

So far, I have stumbled upon many advices and papers on PU Learning and Unary classification. TLDR: Does anyone have suggestions for specific algorithm or implementation for labeled data of only one ...
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3 votes
1 answer
177 views

Probability for label correctness in semi-supervised learning

I am aware of the existence of semi-supervised learning approaches, such as the Ladder Network, where only a subset of the data is labeled. Are there any methods or papers which consider correctness ...
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5 votes
2 answers
688 views

General strategy for imbalanced, semi-supervised, sparse problem

I am looking for some general advice on where to start with this problem. There are 350 sparse (low positive integer) features. I have 2000 positives, 1000 negatives, and infinite unlabeled data, ...
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