# Tag Info

## Hot answers tagged multi-label-classification

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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 ($\mathrm {TP}$) and true negatives ($\mathrm {TN}$), so the usual formula to calculate the accuracy is the following one (your first one). \begin{align} \text{Accuracy}=\frac {\...

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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 features), a hidden layer H which does not have a fixed size, and output O of length c (number of classes). Then we have 3 extra vectors than usual: a vector U they ...

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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 MLP, because Fisher Faces will be increasingly difficult if adding more individuals or classes. You can make a simple MLP model, because it just has 3 layers ...

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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 network outputs, that can be translated as "don't know what to do". In other words, the network can result in: "I don't know what to do now&...

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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 categorize "Adding Class Weights for an imbalanced dataset" under the technique "Cost sensitive learning": Cost sensitive learning. This ...

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I know this is not a straight answer to your question, but I couldn't comment on your post so decided to post it (so maybe I will delete it after you received a better answer). I think this playlist by sentdex can be handy as he goes through a lot of details to teach a neural network model that can drive cars in GTA-V by simply looking at each frame of the ...

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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 reducing the length of all the sentences to the length of the shortest sentence in the corpus. This is generally not done as it reduces the amount of information ...

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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.

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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 layers. An MLP has input layer, at least one hidden layer, and output layer. Multilayer perceptron is one method many used. one of them, regards research on ...

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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 shape [batch_size, time_steps, n_features], which is the format required by all main DL libraries (pytorch, keras and tensorflow). I linked below 2 tutorials ...

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