Questions tagged [supervised-learning]

For questions related to supervised learning.

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31 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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8 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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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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1answer
48 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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26 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
73 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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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
24 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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2answers
270 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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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 ...
2
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1answer
91 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 $3$, ...
2
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1answer
455 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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26 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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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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1answer
154 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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0answers
52 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
136 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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42 views

How to train an Encoder-Decoder LSTM for sequence to sequence prediction?

I have a dataset where for each country there is a name (string) and a multivariate time series (all integers). I am trying to use an Encoder-Decoder LSTM to forecast the next time steps in the time ...
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24 views

Input with variable length Classification problem

I have a dataset with patient information with discrete labels (labels are stages of a particular disease) which needs to be predicted (Basically a classification problem). The data set looks ...
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1answer
36 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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0answers
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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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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2answers
199 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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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
47 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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1answer
70 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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2answers
289 views

Is it possible to guide a reinforcement learning algorithm?

I have just started to study reinforcement learning and, as far as I understand, existing algorithms search for the optimal solution/policy, but do not allow the possibility for the programmer to ...
2
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1answer
93 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
89 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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1answer
73 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 ...
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0answers
46 views

Model for supervised sequence classification task

The Problem I am currently working on a sequence classification problem I try to solve with machine learning. The target variable is the current state of a system. This target variable is following a ...
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1answer
59 views

Solving the supervised learning problem of learning $p(y \vert \mathbf{x})$ by using traditional unsupervised technologies to learn $p(\mathbf{x}, y)$

I am currently studying Deep Learning by Goodfellow, Bengio, and Courville. In chapter 5.1.2 The Performance Measure, $P$, the authors say the following: Unsupervised learning and supervised learning ...
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0answers
33 views

Specific Neural Network Subtype for Automatic Web Scraping (Hyperlink Identification)

For a personal project, I'm trying to download files from a specific set of websites using a web scraper. The scraper has to navigate multiple webpages to get to the files I want to download. I'd like ...
2
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1answer
203 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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0answers
41 views

How can I build a model to approximate the function $f(n) = 2n$?

I made the following HTML nd javascript to predict $f(n) = 2n$. Basically, I am trying to design my first neural network which predicts 2 multiplied by a number. I know we don't need a neural network ...
2
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1answer
100 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 ...
4
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1answer
61 views

How could decision tree learning algorithms cope with imbalanced classes?

Decision trees and random forests may or not be more suited to solve supervised learning problems with imbalanced labels (or classes) in datasets. For example, see the article Using Random Forest to ...
3
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1answer
109 views

What needs to be done to make a fair algorithm?

What needs to be done to make a fair algorithm (supervised and unsupervised)? In this context, there is no consensus on the definition of fairness, so you can use the definition you find most ...
3
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2answers
664 views

What is the difference between reinforcement learning and AutoML?

My vague understanding of reinforcement learning (RL) is that it's very similar to supervised learning except that it updates on a continuous feed of data/activity, this to me sounds very similar to ...
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2answers
70 views

What does “immediate vector-valued feedback” mean?

In the book Artificial Intelligence Engines: A Tutorial Introduction to the Mathematics of Deep Learning, James Stone says With supervised learning, the response to each input vector is an output ...
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1answer
152 views

Class imbalance and “all zeros” one-hot encoding?

I tried this example for a multi class classifier, but when looking at the data I realized two things: There are many examples of "all zeros" vectors, that is, messages that don't belong in any ...
5
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1answer
184 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 ...
2
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1answer
76 views

What are the most common methods to enable neural networks to adapt to changing environments?

For real applications, concept drifts often exist, i.e., the relationship between the input and output changes overtime. Thus, we need our AI or machine learning system to quickly adapt to the ...
3
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1answer
1k views

Are the training loss and validation loss plotted per sample or per batch?

I am using a CNN to train on some data, where training size = 21700 samples, and test size is 653 samples, and say I am using a batch_size of 500 (I am accounting for samples out of batch size as well)...
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0answers
133 views

What are the differences between CRF and HMM?

What I know about CRF is that they are discriminative models, while HMM are generative models, but, in the inference method, both use the same algorithm, that is, the Viterbi algorithm, and forward ...
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3answers
58 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 $...