I was reading the papers on deep learning. Most of them refer to unsupervised learning. They also say the neurons are pre-trained using unsupervised RBM network. Later they are fine tuned us...

I researched some materials，and know that the goal of contrastive learning and metric learning are both to learn such an embedding space in which similar sample pairs stay close to each other while...

I have an imbalanced dataset containing a binary classification problem. I have built Random Forest Classifier and used k-fold cross-validation with 10 folds. kfold = model_selection.KFold(n_splits...

I am working on a classification problem using CNN where my input image size is 64X64 and I want to use pretrained model such as VGG16,COCO or any other. But the problem is input image size of pret...

As I understand it when creating a supervised learning model, our model may have high bias if we are making very simple assumptions (for example if our function is linear) which cause the alg...

Which are the fundamental criterias for using supervised or unsupervised learning? When is one better than the other? Is there specific cases when you can only use one of them? Thanks

My problem: I have a dataset which is a large JSON file. I read it and store it in the trainList variable. Next, I pre-process it - in order to be able to work with it. Once I have done that I s...

I'm learning sklearn and I didn't understand very good the difference and why use 4 outputs with the function train_test_split(). In the Documentation, I found some examples but it wasn't sufficien...

All or nearly all of the papers using dropout are using it for supervised learning. It seems that it could just as easily be used to regularize deep autoencoders, RBMs and DBNs. So why isn't dropou...

I would like to calculate AUC, precision, accuracy for my classifier. I am doing supervised learning: Here is my working code. This code is working fine for binary class, but not for multi class. ...

I have a ML model which is trained on a million data set (supervised classification on text) , however I want the same model to get trained again as soon as a new data comes in (training data). Thi...

In terms of artificial intelligence and machine learning, what is the difference between supervised and unsupervised learning? Can you provide a basic, easy explanation with an example?

I'm trying to use the packages quanteda and caret together to classify text based on a trained sample. As a test run, I wanted to compare the build-in naive bayes classifier of quanteda with the on...

I would like to study the optimal tradeoff between bias/variance for model tuning. I'm using caret for R which allows me to plot the performance metric (AUC, accuracy...) against the hyperparameter...

I recently studied about supervised learning and unsupervised learning. From theory, I know that supervised means getting the information from labeled datasets and unsupervised means clusteri...

How can I use Subsequence String Kernel (SSK) [Lodhi 2002] to train a SVM (Support Vector Machine) in Python?

I'm working on a problem that involves classifying a large database of texts. The texts are very short (think 3-8 words each) and there are 10-12 categories into which I wish to sort them. For the ...

I have been working through the concepts of principal component analysis in R. I am comfortable with applying PCA to a (say, labeled) dataset and ultimately extracting out the most interesting firs...

Could someone explain the difference between classification and pattern recognition as simply as possible or direct me to the right place?!

I have very small data that belongs to positive class and a large set of data from negative class. According to prof. Andrew Ng (anomaly detection vs supervised learning), I should use Anomaly dete...

According to my understanding, Distant Supervision is the process of specifying the concept which the individual words of a passage, usually a sentence, are trying to convey. For example, a datab...

I've built an MLP with Google's TensorFlow library. The network is working but somehow it refuses to learn properly. It always converges to an output of nearly 1.0 no matter what the input actually...

I have a document with tagged data in the format Hi here's my [KEYWORD phone number], let me know when you wanna hangout: [PHONE 7802708523]. I live in a [PROP_TYPE condo] in [CITY New York]. I wan...

I have been following the tutorials on DeepLearning.net to learn how to implement a convolutional neural network that extracts features from images. The tutorial are well explained, easy to u...

I am using Scikit-learning and I need to calculate the True positive (TP), the False Positive (FP), the True Negative (TN) and the False Negative (FN) from a confusion matrix like this: [[2 0 3 4]...