Questions tagged [supervised-learning]

For questions related to supervised learning.

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7
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1answer
551 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-...
1
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2answers
818 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
177 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 ...
2
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4answers
134 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 ...
1
vote
3answers
199 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 ...
9
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3answers
5k 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?
5
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1answer
983 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 ...
1
vote
1answer
696 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 ...
2
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0answers
153 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 ...
1
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0answers
40 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
22 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 ...
5
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2answers
101 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 $[...
1
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0answers
35 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 ...
61
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3answers
55k views

What is self-supervised learning in machine learning?

What is self-supervised learning in machine learning? How is it different from supervised learning?
1
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2answers
1k 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 ...
3
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2answers
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 ...
5
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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 ...
9
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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 ...
8
votes
1answer
407 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 ...
3
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1answer
137 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
396 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 ...

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