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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 ...

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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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48 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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31 views

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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12 views

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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6 views

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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22 views

(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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21 views

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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17 views

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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57 views

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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77 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
51 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
147 views

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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91 views

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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20 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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3answers
133 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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1answer
135 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
75 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
25 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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22 views

Having trouble understanding the purpose of the shadow vertex in Adsorption

Here is the paper I am currently reading: http://www.esprockets.com/papers/adsorption-yt.pdf And here is the section that is confusing me: ...
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45 views

classification with heckman selection

I have a random sample of observations $X$, where $X$ is an $N \times K$ matrix of predictors. For some subset of $X$, $X_o$ I observe my substantive binary outcome of interest $y_o$, and for the rest ...
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1answer
43 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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34 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
296 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)
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131 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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83 views

Is the process of re-training a supervised model (decision tree) by appending new data Dynamic?

Problem description We want to predict the prices of airline tickets for a specific week. We have a dataset that contains all the prices from all the prior weeks till the week where we want to do ...
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2answers
607 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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50 views

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
448 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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76 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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1answer
170 views

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
38 views

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
514 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
51 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
664 views

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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225 views

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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24 views

A machine learning method that can use two labeled data sets of different quality for training?

Let's say that I've two types of labeled data for a classification problem. The setup is the following: Raw data set R a lot of raw data with hundreds of features. Labeled data set A A small ...
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1answer
2k views

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
536 views

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 ...
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1answer
706 views

How to calculate threshold value in supervised principal component analysis?

How to calculate the threshold value for selecting the number of features using supervised principal component analysis? I have a $152\times 27578$ data matrix and I want to apply SPCA to reduce the ...
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4answers
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“Semi supervised learning” - is this overfitting?

I was reading the report of the winning solution of a Kaggle competition (Malware Classification). The report can be found in this forum post. The problem was a classification problem (nine classes, ...
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632 views

How can I assess performance of a semi-supervised learning method?

I'm working with a semi-supervised learning task, where I only have positive and unlabelled data (PU learning). I've tested a few algorithms and would like to assess their performance. For ...
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1answer
83 views

Why is Matrix A in Metric Learning expected to be PSD?

I read that in Metric learning the metric A is supposed to PSD due to the non-negativity constraint on the distance. My question is how does A being a PSD (positive semi-definite) ensures that the ...
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Extreme case of semi supervised learning with entire possible population

I don't know how to exactly frame the title of the question. What I think I have is an extreme case of semi supervised learning. There are 24 features and I have labelled data for around 70 data-...
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4answers
831 views

Semi supervised classification with unseen classes

Consider the following problem. You have a large dataset, some small subset of which have labels from the classes A, B and C. I would like to classify the unlabelled subset of items each of which can ...
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3answers
3k views

How to predict outcome with only positive cases as training?

For the sake of simplicity, let's say I'm working on the classic example of spam/not-spam emails. I have a set of 20000 emails. Of these, I know that 2000 are spam but I don't have any example of not-...
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1answer
681 views

Incorporate new unlabeled data into classifier trained on a small set of labeled data

I have a set of 400 labeled samples (8 numeric features) on which I trained a binary classifier. The problem I am facing is that once the classifier is shipped to the users, I will get additional ...
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2answers
427 views

How to find weights for a dissimiliarity measure

I want to learn (deduce) attribute weights for my dissimilarity measure that I can use for clustering. I have some examples $(a_i,b_i)$ of pairs of objects that are "similar" (should be in the same ...
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1answer
1k views

What are the benefits for semi-supervised learning over unsupervised clustering? Or any limitations?

I have another question about semi-supervised learning vs unsupervised clustering, what are the benefits and limitations? I have got some data with labels and some without labels. I performed semi-...
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314 views

Semi-supervised learning vs supervised learning, what are the benefits and limitations?

Just wondering if any previous work compared semi-supervised learning vs supervised learning? Currently, I have got both datasets with and without labeling. And therefore, it is intuitive for me to ...