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4 votes
Accepted

Why is there more than one way of calculating the accuracy?

In machine learning, the accuracy is usually defined as the number of correct predictions divided by the total number of predictions. The correct predictions are the true positives ($\mathrm {TP}$) ...
nbro's user avatar
  • 40.8k
3 votes
Accepted

Why is the validation loss less than the training loss, and what can be said about the effect of the learning rate?

This is very difficult to tell with the information provided, but the phenomenon is something that I have encountered many times before. Sometimes this is not a bad thing, here are some possible ...
PMaynard's user avatar
  • 248
2 votes
Accepted

Can I do image classification with Multi Layers Perceptron (MLP)?

Multi Layers Perceptron(MLP) can be used for image classification, but it has a lot of deficiency than Convolutional Neural network(CNN). But if you compare MLP and Fisher Faces , the better one is ...
Firhan maulana rusli's user avatar
2 votes
Accepted

What are pros and cons of using a multi-head neural network versus a single neural network for multi-label classification?

If I understood things correctly: You have a task which you need to estimate two values, gender and age. Your question revolves about the difference between networks which share layers for both inputs,...
João Schapke's user avatar
2 votes
Accepted

Are the labels updated during training in the algorithm presented in "An algorithm for correcting mislabeled data"?

I think that making some draws might help. Below I tried to draw the model architecture. We start with classic feed-forward structure: input represented by a vector I with length f (number of ...
Edoardo Guerriero's user avatar
1 vote

Multilabel text classification with highly imbalanced training data

The architecture selection is reasonable. BERT itself has plenty of parameters. There is no need to use anything more complex. If the labels are mutually exclusive, you should use softmax + ...
Jindřich's user avatar
  • 391
1 vote

Image classification problem with multiple right classes

This is a good question. There are definitely good reasons for wanting a loss function that evaluates whether at least one of the classes was picked up by the model. To do what you are attempting, I ...
Snehal Patel's user avatar
1 vote
Accepted

How to define a loss function for multi-label problem?

Given your response in the comments, you are faced with a semi-supervised learning problem where you have a small set of data with ground truth labels, and a large set of data without ground truth ...
Raphael Lopez Kaufman's user avatar
1 vote

How to define a loss function for multi-label problem?

If I understand correctly, the training data are voice messages which contain one or more suggested labels for the class that the voice message belongs to. Even though there are multiple suggestions, ...
dmc-au's user avatar
  • 21
1 vote

What is the difference between multi-label and multi-task classification?

Is there any fundamental difference between the two? The difference is in the names: Multi task means that we are learning more than a single task, i.e. the labels we have will be used to compute ...
Edoardo Guerriero's user avatar
1 vote
Accepted

Is it valid to implement hyper-parameter tuning and THEN cross-validation?

You should not use the training data for hyper-parameter tuning. In other words, when doing the hyper-parameter tuning, you should not optimize the training objective. You should optimize an objective ...
nbro's user avatar
  • 40.8k
1 vote
Accepted

Is this neural network with a softmax in the output layer suitable for multi-label classification?

Firstly, you should use sigmoid in your last layer instead of softmax. Softmax returns a probability distribution, meaning that when one labels probability increases the other will decrease, which is ...
razvanc92's user avatar
  • 1,148
1 vote

What does "adding class weights for an imbalanced dataset" mean in the case of multi-label classification?

The paper A systematic study of the class imbalance problem in convolutional neural networks is a great overview on class imbalance approaches. Section 2 summarizes various methods commonly used. They ...
user3667125's user avatar
  • 1,570
1 vote
Accepted

Should I use additional empty category in some categorical problems?

In short: yes, you must allow "do nothing" decision as a first level result. Your system must decide the action to be taken, including "do nothing" action. This is different to low ...
pasaba por aqui's user avatar
1 vote
Accepted

How do RNN's for sentiment classification deal with different sentence lengths?

One of the essential pre-processing we do on the corpus involves treating the variable-length sentences to a fixed length. There are various ways in which we can do this: Truncate This involves ...
Saurav Maheshkar's user avatar
1 vote

Can I do image classification with Multi Layers Perceptron (MLP)?

Depends, if the faces are centered and have the same background yes. You also need a lot of data. If they are daily life images, then no. You will have very bad generalization.
FourierFlux's user avatar
1 vote

Can I do image classification with Multi Layers Perceptron (MLP)?

let me try to answer your question. yes, you can use multilayer perceptron to image classification. Multilayer Perceptron is topology the most common of ANN, where perceptrons are connected to form ...
Muhammad Faiq A.'s user avatar
1 vote

How to train a LSTM with multidimensional data

I don't see any special characteristic in the problem you're posing. Any LSTM can handle multidimensional inputs (i.e. multiple features). You just need to prepare your data such as they will have ...
Edoardo Guerriero's user avatar
1 vote

Recent algorithms for correcting mislabeled data using multilayer perceptrons

The most general solution today for the problem of finding label errors in datasets is called "confident learning" which works for all datasets and models, can be run time-efficiently in one ...
cgnorthcutt's user avatar

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