Questions tagged [semi-supervised-learning]

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

22 questions with no upvoted or accepted answers
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1answer
38 views

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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1answer
21 views

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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319 views

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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0answers
20 views

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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0answers
9 views

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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0answers
23 views

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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1answer
34 views

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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21 views

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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0answers
22 views

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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0answers
55 views

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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0answers
103 views

Semi-supervised Learning doubt

I'm reading "Hands on machine learning" by Aurelien Geron. He stated that semi-supervised learning is: Some photo-hosting services, such as Google Photos, are good examples of this. Once you ...
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1answer
434 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 views

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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9 views

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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6 views

How should business rules be used to add to small, existing training dataset? (Semi Supervised Learning)

Problem Statement: Take 1200 labeled users and project them to the rest of our userbase (3.6M) What I've done: Leveraged business rules to add to existing dataset. After doing so, I'm left with the ...
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8 views

Looking for feedback on a semi-supervised learning approach for multi-class classification

Problem: Currently only have 1200 labeled (3-classes) customers with an entire customer base of 4.7M. Just leveraging the 1200 to train the model isn’t generating sufficient results so I’m now looking ...
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0answers
10 views

List of major ML algorithms with code

Can anyone recommend me any GitHub code repo/s or textbook/s that walks through all the major ML algorithms in great detail with code to get hands-on with? I have a data engineering background and ...
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1answer
98 views

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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0answers
30 views

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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34 views

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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3answers
850 views

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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1answer
875 views

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 ...