Questions tagged [labels]

For questions related to the labels associated with data points.

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2
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0answers
26 views

Is soft labeling the same thing as label smoothing?

I have some data with soft labels and I am trying to figure out the best approach to solve the problem with Machine Learning (since regular classification is of the table, i.e. hard labels). However, ...
0
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1answer
30 views

Should I label static objects on video dataset?

I'm using nvidia Transfer Learning Toolkit to detect cars in some video frames. I found some dataset (for example https://www.jpjodoin.com/urbantracker/dataset.html and https://www.kaggle.com/...
1
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2answers
116 views

What is the difference (if any) between semantic segmentation and multi-class, mutually exclusive classification?

Multi-class classification is simply assigning all data points into one of up to any finite number of mutually exclusive labels. I am new to the field(s) of AI/ML and I keep hearing people use the ...
0
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0answers
29 views

What is it called when every entry of the output vector has a meaning?

I'm trying to implement Deep Q Learning using Tensorflow. The input is a vectorized representation of the state, and the output is a vector whose length is the number of possible actions. I've already ...
0
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1answer
28 views

Is it possible to flip the features and labels after training a model?

The goal of this program is to predict a game outcome given a game-reference-id, which is a serial number like so: id,totalGreen,totalBlue,totalRed,totalYellow,...
8
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1answer
9k views

What is the definition of "soft label" and "hard label"?

In semi-supervised learning, there are hard labels and soft labels. Could someone tell me the meaning and definition of the two things?
3
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1answer
66 views

When working with time-series data, is it wrong to use different time-steps for the features and target?

When working with time-series data, is it wrong to use daily prices as features and the price after 3 days as the target? Or should I use the next-day price as a target, and, after training, predict 3 ...