Newest 'semi-supervised-learning' Questions

2

votes

1answer

21 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 ...

asked Dec 6 at 16:44

A.White

575 bronze badges

1

vote

1answer

13 views

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 ...

keras cnn convnet encoding semi-supervised-learning

asked Nov 29 at 6:34

Siddhant Kundu

116 bronze badges

0

votes

0answers

10 views

Is it wise to include the target labels when a supervised learning problem is tackled as an unsupervised learning problem?

I have a problem which requires both a supervised and unsupervised learning approach. This is because I am trying to find some hidden clusters (if they even exist) beyond the labels of the dataset. ...

unsupervised-learning supervised-learning semi-supervised-learning

asked Oct 9 at 14:08

Mysterio

1012 bronze badges

2

votes

3answers

38 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 ...

clustering unsupervised-learning supervised-learning semi-supervised-learning

asked Sep 22 at 18:46

Rodolphe LAMPE

1212 bronze badges

0

votes

0answers

4 views

Questions about LabelPropagation

I have been tasked with identifying which of our customers are most similar to a specific subset. My instinct was to treat this as a semi-supervised problem and use scikit-learn's LabelPropagation ...

machine-learning scikit-learn semi-supervised-learning

asked Sep 2 at 9:43

Dan Scally

1,2491 silver badge19 bronze badges

0

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0answers

10 views

Using Unsupervised / Supervised Learning for RGB manipulation

I need to teach a model(or any substitute) for automatically adjusting the concentration of R, G, and B values until they make up white colour. i.e, tweak the RGB values (beginning from some arbitrary ...

machine-learning machine-learning-model unsupervised-learning supervised-learning semi-supervised-learning

asked Aug 20 at 16:14

Pe Dro

1

2

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0answers

89 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 ...

semi-supervised-learning

asked Jul 22 at 13:11

Poete Maudit

8674 silver badges18 bronze badges

0

votes

0answers

28 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 ?

machine-learning sentiment-analysis transfer-learning semi-supervised-learning

asked Jul 15 at 22:36

mtesta010

233 bronze badges

0

votes

1answer

36 views

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) ...

clustering image-classification supervised-learning multilabel-classification semi-supervised-learning

asked Jul 7 at 14:34

Travis Black

1112 bronze badges

0

votes

0answers

28 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 ...

classification unbalanced-classes semi-supervised-learning

asked Jun 13 at 19:55

Stu

1014 bronze badges

0

votes

0answers

16 views

Co-training independent views

I found a tutorial on co-training that mentions that I need two views for training, which are conditionally independent. Could someone explain to me what this means regarding datasets?

dataset semi-supervised-learning

asked Jun 8 at 22:00

SMI9

114 bronze badges

0

votes

3answers

155 views

What kind of learning in this training situation when 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 ...

unsupervised-learning supervised-learning anomaly-detection semi-supervised-learning

asked May 12 at 14:12

disney82231

674 bronze badges

0

votes

0answers

17 views

what is clamping in label propagation

I'm not clear about what the clamping does in label propagation. I checked the document in scikit learn. label propagation What does it meany by change the weight of the true ground labeled data ...

semi-supervised-learning

asked May 6 at 17:13

Tracy Yang

11 bronze badge

0

votes

0answers

32 views

How to deal with small amount of labeled samples?

I'm trying to develop skill to deal with very small amount of labeled samples (250 labeled/20000 total, 200 features) practicing on Kaggle "Don't Overfit" dataset (Traget_Practice have provided all 20,...

machine-learning classification feature-selection ensemble-modeling semi-supervised-learning

asked May 4 at 6:45

FirePower

14 bronze badges

0

votes

1answer

58 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 ...

machine-learning data-mining semi-supervised-learning active-learning

asked May 2 at 8:35

MiNdFrEaK

1215 bronze badges

1

vote

1answer

29 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 ...

accuracy semi-supervised-learning

asked Mar 23 at 13:10

SMI9

114 bronze badges

0

votes

1answer

71 views

How to get probability of classification

I have binary classification, I tried several model KNN, SVM, decision tree and random forest. I have 50 000 samples, X_train has 50 000 rows and 2300 columns. ...

machine-learning classification semi-supervised-learning

asked Mar 20 at 21:07

Max

31 bronze badge

1

vote

0answers

11 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 ...

svm semi-supervised-learning implementation

asked Feb 13 at 22:25

Nicolas Scotto Di Perto

1313 bronze badges

2

votes

0answers

15 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 ...

neural-network image-classification supervised-learning semi-supervised-learning

asked Dec 28 '18 at 20:36

amlwwalker

1212 bronze badges

1

vote

0answers

50 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 ...

machine-learning python nlp supervised-learning semi-supervised-learning

asked Dec 27 '18 at 11:34

Khushhalm

113 bronze badges

0

votes

1answer

31 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 ...

semi-supervised-learning neural

asked Dec 18 '18 at 16:28

Andreas Look

7682 silver badges13 bronze badges

0

votes

1answer

157 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 ...

regression semi-supervised-learning

asked Nov 4 '18 at 10:25

rnso

1,0121 gold badge6 silver badges22 bronze badges

0

votes

1answer

89 views

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 ...

predictive-modeling feature-selection unsupervised-learning supervised-learning semi-supervised-learning

asked Oct 9 '18 at 8:14

Lizou

852 silver badges7 bronze badges

2

votes

1answer

378 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 ...

python clustering word2vec semi-supervised-learning

asked Aug 7 '18 at 13:15

Nicolò Gasparini

1856 bronze badges

1

vote

0answers

26 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 ...

neural-network deep-learning ensemble-modeling regularization semi-supervised-learning

asked Aug 4 '18 at 19:48

MiloMinderbinder

4052 silver badges11 bronze badges

1

vote

0answers

58 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 ...

machine-learning statistics unsupervised-learning supervised-learning semi-supervised-learning

asked Jul 19 '18 at 14:54

gdlzzzz

233 bronze badges

3

votes

2answers

230 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 ...

machine-learning classification time-series feature-engineering semi-supervised-learning

asked Jul 3 '18 at 5:33

Keith

2961 silver badge12 bronze badges

0

votes

1answer

311 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 ...

machine-learning python deep-learning supervised-learning semi-supervised-learning

asked Jun 3 '18 at 12:53

Giorgi Jambazishvili

1195 bronze badges

1

vote

1answer

32 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 ...

machine-learning python clustering k-means semi-supervised-learning

asked Apr 24 '18 at 8:01

IS2057

2391 gold badge5 silver badges20 bronze badges

-1

votes

1answer

473 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 ...

scikit-learn regression semi-supervised-learning

asked Apr 5 '18 at 18:55

ddd

1631 silver badge4 bronze badges

0

votes

1answer

60 views

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, ...

clustering unsupervised-learning semi-supervised-learning bias

asked Apr 5 '18 at 16:55

TheProletariat

1415 bronze badges

4

votes

1answer

7k views

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) ...

keras loss-function semi-supervised-learning

asked Mar 1 '18 at 5:32

Tian

411 silver badge3 bronze badges

5

votes

4answers

4k views

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 ...

machine-learning classification semi-supervised-learning

asked Jan 17 '18 at 14:54

Ricardo Cruz

2,6027 silver badges28 bronze badges

0

votes

0answers

49 views

Having measurements of a medical instrument how to improve model with demographic data?

There is a medical instrument that produces various measurements. These measurements are being used as inputs to a model to classify the health condition of a patient. The health condition is binary, ...

classification bayesian semi-supervised-learning demographic-data

asked Nov 27 '17 at 19:49

Georgios Pligoropoulos

6133 silver badges15 bronze badges

1

vote

1answer

374 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 ...

python classification tensorflow svm semi-supervised-learning

asked Jul 27 '17 at 20:13

Flair

1211 bronze badge

3

votes

1answer

143 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 ...

supervised-learning unsupervised-learning labels semi-supervised-learning

asked Jun 9 '17 at 12:46

AlexGuevara

1493 bronze badges

5

votes

2answers

558 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, ...

machine-learning predictive-modeling semi-supervised-learning

asked Dec 28 '16 at 16:07

Lucky711

513 bronze badges

5

votes

3answers

141 views

Predictive clustering

I have an hypothesis but i don't know if it's true. If the cluster is dense and we apply a supervised learning on this data, the model generated by this cluster will be more efficient for new data ...

clustering supervised-learning semi-supervised-learning

asked Mar 6 '16 at 15:46

KyBe

3802 silver badges12 bronze badges

2

votes

1answer

210 views

Generic strategy for object detection

I have a huge collection of objects from which only a tiny fraction are in a class of interest. The collection is initially unlabelled, but labels can be added using an expensive operation (for ...

machine-learning classification object-recognition semi-supervised-learning active-learning

asked Mar 6 '15 at 10:28

Valentas

5064 silver badges16 bronze badges

11

votes

3answers

2k views

Build a binary classifier with only positive and unlabeled data

I have 2 datasets, one with positive instances of what I would like to detect, and one with unlabeled instances. What methods can I use ? As an example, suppose we want to understand detect spam ...

classification semi-supervised-learning

asked Jul 7 '14 at 9:34

nassimhddd

5272 silver badges12 bronze badges

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