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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Is using Probability Classification to predict whether a restaurant will purchase the best approach?

I have a data set that contains data about restaurants in the United states including menu, foot traffic, type of cuisine, type of restaurant, and other restaurant attributes. I also have a small ...
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can't get higher Training Accuracy with Semi-Supervised Learning

I am currently training my model using a combination of labeled and unlabeled samples in the realm of semi-supervised learning. However, as I evaluate the model's training accuracy on labeled samples ...
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Improving performance of anomaly detection using dataset?

I am leveraging an isolation forest model from the scikit-learn library for anomaly detection in a time series dataset where each point in the dataset is a data frame. However, I possess additional ...
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Looking for a way to train a model to learn parameters for clustering

I have 5000 docs, each is a review. For each review, i'm plotting the sentences in a semantic dimension. Now, I'm applying clustering to these points for each review. The success of my model depends ...
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Machine learning can be broken down into supervised, unsupervised, and reinforcement learning. Is there anything else?

Is it even logically possible to have a type of machine learning that doesn't fall into those three paradigms? Supervised: a dataset of inputs and outputs are fed to an algorithm which learns a ...
Austin Capobianco's user avatar
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Identifying recurring transaction clusters (subscriptions) at a user level

I need some help converting this issue into a machine learning problem. Goal: Grouping credit charges into clusters of recurring transactions per user Input data: List of credit card charges with <...
Fintech Pikachu's user avatar
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What is the state-of-the-art in prediction\classification missing labels in partially labeled data?

Overview Let's say I have the following data: ...
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What kind of learning do I need ? (use-case specific)

Consider a scenario where I have a model trained on gesture videos (say a 3D ResNet). I am looking for a technique (or a combination) that allows me to further train the model every time I have a new ...
batman's user avatar
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Is it acceptable to perform inference on unlabeled data utilized during SSL training

I am facing a classification problem and I have both labeled and unlabeled data. To make use of the unlabeled data, I have chosen to adopt the self-learning approach, which is a type of semi-...
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How can we integrate zero shot classification with a supervised classifier?

We have two sets of labels that are known and unknown to the supervised classifier (SC). We infer for a test example using the SC and a zero shot classifier (ZC). Let's assume, our inference datapoint ...
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Topic classification on text data with no/few labels

I would like to achieve a classification of a text input into predefined categories. From what I have understand unsupervised approach are unfeasible if my target label is something very rare in ...
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Why only discrete labels are used for (semi-)supervised VAEs?

I've noticed all semi-supervised VAEs assume discrete (categorical) labels to encourage disentangled representation learning in VAEs. e.g., Kingma, Durk P., et al. "Semi-supervised learning with ...
MerelyLearning's user avatar
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Why weak supervision works?

I've learned that weak supervision combines multiple labeling methods to generate labels for a large dataset. I can't understand why generated labels can be used to train a more accurate model than ...
Neo's user avatar
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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 ...
lamyvista's user avatar
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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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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 ...
Demetri Smith's user avatar
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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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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-...
Inhyeok Yoo's user avatar
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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 ...
Sandhya Indurkar's user avatar
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1 answer
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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 ...
Piyush Makhija's user avatar
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1 answer
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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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191 views

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 ...
A.White's user avatar
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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 ...
Siddhant Kundu's user avatar
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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 ...
Rodolphe LAMPE's user avatar
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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) ...
Travis Black's user avatar
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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 ...
disney82231's user avatar
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280 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 ...
N. F.'s user avatar
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1 answer
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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 ...
SMI9's user avatar
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1 answer
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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....
Max's user avatar
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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 ...
Nicolas Scotto Di Perto's user avatar
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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 ...
amlwwalker's user avatar
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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 ...
Khushhal's user avatar
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1 answer
36 views

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 ...
Andreas Look's user avatar
1 vote
1 answer
356 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 ...
rnso's user avatar
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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 ...
Lizou's user avatar
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2 votes
1 answer
578 views

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 ...
Nicolò Gasparini's user avatar
3 votes
2 answers
466 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 ...
Keith's user avatar
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1 answer
540 views

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 ...
Giorgi Jambazishvili's user avatar
1 vote
1 answer
44 views

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 ...
IS2057's user avatar
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1 answer
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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 ...
ddd's user avatar
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