Questions tagged [supervised-learning]

For questions related to supervised learning.

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41 views

What problem does the neural network really solve?

In the image below taken from a Youtube video, the author explains that the neural network can be used to fit a relational graph for a set of data points shown by the green line. And that this is ...
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1answer
79 views

Reinforcement learning with industrial continuous process

I am new to RL and wish to realize a RL control for an industrial process. The goal is to control the temperature and humidity in a vegetal food production chamber. States: External temperature and ...
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1answer
96 views

An infinite VC dimensional space vs using hierarchical subspaces of finite but growing VC dimensions

I have the following scenario. I have a binary classification problem, whose underlying function is a step function. The probability distribution of feature vectors is a uniform over the domain. Case ...
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1answer
54 views

Aside from specific training sets, what distinguishes the capabilities of different AI implementations?

(Disclaimer: I don't know much about ML/AI, besides some basic ideas behind it all.) It seems like ML/AI models can often be boiled down to statistics, where certain levers (weights) get fine-tuned ...
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38 views

What are some machine learning frameworks for supervised clustering?

I have a task where I need to take "data points" which consist of collections of items. Each item needs to be categorised according to predefined categories. That's the easy part - my ...
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1answer
116 views

What are the pros and cons of supervised, semi-supervised and unsupervised relation extraction in NLP?

I am following the NLP course taught by Dan Jurafsky. In the video lectures Supervised Relation Extraction and Semi Supervised and Unsupervised Relation Extraction Jurafsky explains supervised, semi-...
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29 views

Non-locally Electrically Programmable Logic Gates - Technological Advances Progress

Preface: I’d like to clarify that I understand what a relay is and that a PLC uses a fairly conventional microprocessor that only digitally establishes logical logic gate configuration as a digitally ...
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29 views

How Long Short Term Memory (LSTM) work for time series classification?

I first got the concept of LSTM on how it works word to word prediction etc. However, I want to know how it work with the time-series classification. For example I have the follwing data (see image ...
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1answer
23 views

Extracting keywords from messages

I'm starting a project where I want to extract keywords from given messages. The keywords are for example something like: "hard disk", "watch" or other technical components. I'm ...
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1answer
41 views

Train a model using a multi-column text-filled excel sheet

I have an excel sheet filled with my own personal appreciations of movies I've watched, and I want to use it to train an AI model so that it can predict if I'll like a specific movie or not, based on ...
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19 views

(explore-exploit + supervised learning ) vs contextual bandits

Lets take an ad recommendation problem for 1 slot. Feedback is click/no click. I can solve this by contextual bandits. But I can also introduce exploration in supervised learning, I learn my model ...
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0answers
44 views

Backpropagation not working as expected

I'm new to neural networks and I try to make a model that is guessing if a point is below or above relative to a function output. The idea is inspired from this video https://youtu.be/DGxIcDjPzac . ...
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30 views

How can my CNN produce an "unknown" label?

I have a dataset of 20k images of infected mango. I have built a web-based app using Flask, where a user can upload a picture, and my CNN model detects the disease. I have 6 classes in the model, ...
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3answers
97 views

If the accuracy of my current model is low ($50 \%$) and we want to minimize time in collecting more data, should we try other models?

Suppose we have a data set with $4,000$ labeled examples. The outcome variable is trinary (three possible categorical values). Suppose the accuracy of a given model is "bad" (e.g. less than $...
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1answer
33 views

How to handle class imbalancing when the actual data are that way

My supervised learning training data are obtained from actual data; and in real cases, there's one class which happens less often than other classes, just around 5% of all cases. To be precise, the ...
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31 views

Is this a supervised or reinforcement learning problem, and which algorithm should I use to solve it?

I have a time series data with a little unusual cost/reward function (I haven't seen it before) The model must predict a $Y$ value for any $X(t)$. The reward is computed as follows. The model will ...
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0answers
9 views

Can you correlate decision boundary of final layer of a neural network to predictive distribution?

I was reading in a On the Decision Boundary of Deep Neural Networks that the final layer of a MLP can be equated to an SVM and can generate decision boundaries similar to methods with SVM. I was ...
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1answer
82 views

Can we use ML to do anything else other than predicting (in the case of mathematical problems)?

(The math problem here just serves as an example, my question is on this type of problems in general). Given two Schur polynomials, $s_\mu$, $s_\nu$, we know that we can decompose their product into a ...
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1answer
108 views

Can supervised learning be used to solve the inverted pendulum problem?

I know that reinforcement learning has been used to solve the inverted pendulum problem. Can supervised learning be used to solve the inverted pendulum problem? For example, there could be an ...
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1answer
418 views

What are the different approaches used in Machine Learning?

There seem to be so many sub-fields, so I'm interested in getting a better understanding of the approaches. I'm looking for information on a single framework per answer, in order to allow for ...
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0answers
19 views

Is there any known approach to generate sets of objects?

I am looking for some known approach, or some previous work, on the following problem: Let $\Sigma$ be an alphabet of symbols and $\Sigma^*$ be the set of all the strings that you can compose from ...
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0answers
30 views

Literature on the advantages of using an auto-encoder for classification

Given a supervised problem with X, y input pairs, one can do two things for obtaining the function f that maps X with y with Neural Networks (and in general in machine learning): Deploy directly a ...
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1answer
98 views

What is the meaning of these equations in Noise2Noise paper?

I am trying to understand what is meant by following equations in the Noise2Noise paper by Nvidia. What is meant by the equation in this image? What is $\mathbb{E}_y\{y\}$? And how should I try to ...
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2answers
2k views

What is "planning" in the context of reinforcement learning, and how is it different from RL and SL?

This is an excerpt taken from Sutton and Barto (pg. 3): Another key feature of reinforcement learning is that it explicitly considers the whole problem of a goal-directed agent interacting with an ...
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36 views

Which models can I use for supervised learning with images?

I have to do a project that detects fabric surface errors and I will use machine learning methods to deal with it. I have a dataset that includes around six thousand fabric surface images with the ...
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1answer
674 views

How to generate labels for self-supervised training?

I've been reading a lot lately about self-supervised learning and I didn't understand very well how to generate the desired label for a given image. Let's say that I have an image classification task, ...
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3answers
67k views

What is self-supervised learning in machine learning?

What is self-supervised learning in machine learning? How is it different from supervised learning?
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1k views

Can a machine learning model predict the pattern of given sequence?

I am curious if it is possible to do so. For example, if I supply $[0, 1, 2, 3, 4, 5]$, the model should return "natural number sequence", $[1,3,5,7,9,11]$, it should return "natural ...
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1answer
1k views

What is the difference between imitation learning and classification done by experts?

In short, imitation learning means learning from the experts. Suppose I have a dataset with labels based on the actions of experts. I use a simple binary classifier algorithm to assess whether it is ...
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2answers
329 views

What is the meaning of "exploration" in reinforcement and supervised learning?

While exploration is an integral part of reinforcement learning (RL), it does not pertain to supervised learning (SL) since the latter is already provided with the data set from the start. That said, ...
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1answer
25 views

Is training a CNN object detector on an image containing multiple targets that are not all annotated will teach it to miss targets?

I want to train a convolutional neural network for object detection (say YOLO) to detect faces. Consider this image: In this training image, I have many people, but only 2 of them are annotated. Is ...
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14 views

ML Algorithm for getting top pick in each sample

I have a dataset of streets - and each street contains several houses. ...
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0answers
14 views

Multiple Inertia sensors system based for gestures recognition

I am a newbie to Machine Learning field as I am engaging to a personal project that I am trying to use the 6 degree of freedom Inertial Measurement Units(IMUs) measuring the Acceleration acting on 3 ...
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1answer
95 views

How can we teach a neural net to make arbitrary data associations?

Let's say I have pairs of keys and values of the form $(x_1, y_1), \dots, (x_N, y_N)$. Then I give a neural net a key and a value, $(x_i, y_i)$. For example, $x_i$ could be $4$ and $y_i$ could be $3$, ...
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3answers
7k views

What is the relation between semi-supervised and self-supervised visual representation learning?

What's the differences between semi-supervised learning and self-supervised visual representation learning, and how they are connected?
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3answers
2k views

Do GANs come under supervised learning or unsupervised learning?

Do GANs come under supervised learning or unsupervised learning? My guess is that they come under supervised learning, as we have labeled dataset of images, but I am not sure as there might be other ...
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0answers
27 views

Finding whether an input column is missing

I am working on a problem similar to this one:(supervised, artificial data) ...
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0answers
40 views

What is a multi channel supervised classifier?

I came across a paper that describes its model architecture in the following way. Our TRIL network is a two-channel network jointly trained to predict the expert’s action given state and the system’s ...
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0answers
46 views

Advantages of training Neural Networks based on analytic success criteria

What is the reason to train a Neural Network to estimate a task's success (i.e. robotic grasp planning) using a simulator that is based on analytic grasp quality metrics? Isn't a perfectly trained NN ...
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0answers
57 views

Why is 'scatter' used instead of variance in LDA?

I've been reading about Fisher's Linear Discriminant Analysis lately, and I noticed that the objective function (particularly for two-class classification) to be maximized contains scatter terms ...
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1answer
280 views

How can supervised learning be viewed as a conditional probability of the labels given the inputs?

In the literature and textbooks, one often sees supervised learning expressed as a conditional probability, e.g., $$\rho(\vec{y}|\vec{x},\vec{\theta})$$ where $\vec{\theta}$ denotes a learned set of ...
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1answer
189 views

How is the reward in reinforcement learning different from the label in supervised learning problems?

How is the notion of immediate reward used in the reinforcement learning different from the notion of a label we find in the supervised learning problems?
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1answer
212 views

Can supervised learning be recast as reinforcement learning problem?

Let's assume that there is a sequence of pairs $(x_i, y_i), (x_{i+1}, y_{i+1}), \dots$ of observations and corresponding labels. Let's also assume that the $x$ is considered as independent variable ...
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1answer
152 views

What is the difference between distant supervision and self-supervision?

Weak supervision is supervised learning, with uncertainty in the labeling, e.g. due to automatic labeling or because non-experts labelled the data [1]. Distant supervision [2, 3] is a type of weak ...
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29 views

Is there a classification task with multiple attribute regression?

I'm trying to look for a task that predicts a discrete label first (classification), and then predicts the multiple continuous attributes of the predicted class. I found some papers about multi-output ...
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2answers
235 views

How can reinforcement learning be unsupervised learning if it uses deep learning?

I was watching a video in my online course where I'm learning about A.I. I am a very beginner in it. At one point in the course, the instructor says that reinforcement learning (RL) needs a deep ...
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0answers
48 views

Can a typical supervised learning problem be solved with reinforcement learning methods?

Let's say I want to teach a neural to classify images, and, for some reason, I insist on using reinforcement learning rather than supervised learning. I have a dataset of images and their matching ...
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1answer
52 views

Is radial basis function network appropriate for small datasets?

I'm a computer engineering student and I'm about to work on my master thesis. My professor gave me a small dataset with brain Computed Axial Tomography records. I would like to use deep learning to ...
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0answers
38 views

How can I approximate a function that determines the priority of objects?

I am facing the following supervised learning problem: An object is fully characterized by its position in $R^n$. There are $m$ objects. There are fully observable (i.e. their positions are always ...
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1answer
75 views

How can I learn a graph given nodes with features in a supervised fashion?

I have a dataset and want to be able to construct a graph from it in a supervised fashion. Let's assume I have a dataset with N nodes, each node has e.g. 10 features. Out of these N nodes, I want to ...