Questions tagged [supervised-learning]

For questions related to supervised learning.

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35 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
31 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
49 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
44 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
25 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 ...
1
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1answer
21 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 $50 \%$). ...
4
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2answers
55 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
150 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
58 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
85 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
104 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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2answers
56 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
182 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
105 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
241 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
65 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
22 views

How would one go about find patterns in text files when keywords are given?

I have a column with links to websites and another column with keywords from those websites. I have to find a link between these two such that for a new input of a website-link, I can generate ...
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0answers
2 views

How to improve the AUC - test of LightBoost based on BayesSearchCv

I need to use LightGBM for improve the auc score of my binary classification algorithm. I have used BayesSearchCv with score:"roc-auc" but i dont know how can i make it able to search the best ...
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 ...
5
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2answers
67 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
8k 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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1answer
114 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 ...
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1answer
357 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 ...