Questions tagged [supervised-learning]

For questions related to supervised learning.

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26 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
44 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
20 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
29 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 ...
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0answers
15 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
44 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 ...
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0answers
35 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 ...
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0answers
40 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 ...
3
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1answer
51 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
77 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
180 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
53 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
53 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 ...
2
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1answer
48 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
61 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 ...
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1answer
116 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
46 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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1answer
24 views

Model Performance and Size of Data Set

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 $50 \%$). ...
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2answers
79 views

How can AI be used to design UI Interfaces?

I'm very new to AI. I read somewhere that AI can be used to create GUI UI/UX design. That has fascinated me for a long time. But, since I'm very new here, I don't have any idea how it can happen. ...
6
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1answer
216 views

Does AlphaZero use Q-Learning?

I was reading the AlphaZero paper Mastering Chess and Shogi by Self-Play with a General Reinforcement Learning Algorithm, and it seems they don't mention Q-Learning anywhere. So does AZ use Q-...
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2answers
295 views

How can I use 1-channel images as input to a CNN?

I need to develop a convolutional neural network whose inputs are 1-channel images, but I dont know how to do it, given that most libraries use 3 channel images. Should I convert my images to RGB? Is ...
3
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1answer
120 views

How Does AlphaGo Zero Implement Reinforcement Learning?

AlphaGo Zero (https://deepmind.com/blog/alphago-zero-learning-scratch/) has several key components that contribute to it's success: A Monte Carlo Tree Search Algorithm that allows it to better search ...
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4answers
113 views

How is regression machine learning?

In regression, in order to minimize an error function, a functional form of hypothesis $h$ must be decided upon, and it must be assumed (as far as I'm concerned) that $f$, the true mapping of instance ...
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3answers
106 views

Recognize pattern in dataset

I'm currently working on a group project where we need to find a pattern in a given dataset. The dataset is a collection of X, Y, Z values of a gyroscope from someone who is walking. If you plot these ...
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2answers
936 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?
3
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1answer
450 views

Variational Autoencoder task for better feature extraction

I have a CNN with the regression task of a single scalar. I was wondering if an additional task of reconstructing the image (used for learning visual concepts), seen in a DeepMind presentation with ...
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1answer
500 views

Why isn't the reverse KL divergence commonly used in supervised learning?

Forward KL Divergence (also known as cross entropy loss) is a standard loss function in supervised learning problems. I understand why it is so: matching a known a trained distribution to a known ...
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0answers
106 views

When to use which metric in machine learning?

In machine learning, there are several metrics to assess the quality of the models: accuracy, precision, recall, f measure, ROC (AUC), etc. There are cases when certain metrics are more appropriate ...
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0answers
35 views

How can I generate keywords associated with a website given its URL?

I have a column with links to websites and another column with keywords from those websites. I have to find a map between these two, such that for a new input, which is a website's URL, I can generate ...
3
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0answers
18 views

Backpropagation: how to take into account different samples quality

I have a NN I'd like to train using supervised learning. Some samples of the training set, however, have better "quality" than others, so I'd like the algorithm to pay "special attention" to them. As ...
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2answers
82 views

What should the range of the output layer be when performing classification?

I am working on a MLP neural networks, using supervised learning (2 classes and multi-class classification problems). For the hidden layers, I am using $\tanh$ (which produces an output in the range $[...
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0answers
29 views

identifying pattern in datasets

i am new to machine learning. i'm trying to identify driving pattern through accelerometer and gyroscope sensor. i have been collecting the data of both the sensors and have been storing them in .csv ...
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3answers
21k 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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2answers
785 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 ...
6
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2answers
323 views

What are 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 ...
2
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1answer
120 views

Why are neural networks always trained “by themselves”?

In the current rush of artificial intelligence research, fueled by NN, independent of the paper I choose, the NN are always trained by themselves. Sure, there are architectures that combine CNN and ...
1
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1answer
373 views

Does neuroevolution require a labelled dataset?

A neuroevolution algorithm, such as DXNN, can be used to refine the topology and weights of an artificial neural network (ANN). The GA will require a fitness function, which means you need labeled ...