# Tag Info

### What are the domains where SVMs are still state-of-the-art?

State-of-the-art is a tough bar, because it's not clear how it should be measured. An alternative criteria, which is akin to state-of-the-art, is to ask when you might prefer to try an SVM. SVMs have ...
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### Are the shortcomings of neural networks diminishing?

Neural Networks have other short comings as well. It takes much longer and far more resources to train a neural network than something like a random forest. So if you need speed of training or are ...
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### What are the domains where SVMs are still state-of-the-art?

Deep Learning and Neural Networks are getting most of the focus because of recent advances in the field and most experts believe it to be the future of solving machine learning problems. But make no ...
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### What is a support vector machine?

I find the chapter on machine learning from Russell & Norvig is a pretty good place to start with SVMs. I think this is Chapter 18? One way to understand an SVM is as a kind of neural network, ...
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### Are the shortcomings of neural networks diminishing?

Just to add to what has been said in @MikeWise's brilliant answer, All things equal, deep learning models generally rank supreme when compared to other algorithms as the size of the dataset increases:...

### Why does training an SVM take so long? How can I speed it up?

The most likely explanation is that you're using too many training examples for your SVM implementation. SVMs are based around a kernel function. Most implementations explicitly store this as an NxN ...
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### How do I use a taxonomy and the support vector machine for question classification?

This is not an answer (I don't have enough reputation to comment). I did something close to this in my master's thesis and think it is close to what you are interested in. In it, I had developed a ...
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### What is the definition of the hinge loss function?

The hinge loss/error function is the typical loss function used for binary classification (but it can also be extended to multi-class classification) in the context of support vector machines, ...
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### Why would LDA have performed much better than SVM and Naive Bayes in diagnosing ADHD?

It would be hard to tell if you don't provide what kind of data/problem you are working on, but LDA works well when data that are grouped in gaussian blobs surrounding centroids while vanilla SVM ...
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### What is the smoothness assumption in SVMs?

Smoothness here is the mathematical definition, so as you implied smoothness is ruled out by output data with sharp spikes or discontinuous jumps (and possibly the data of the gradient, the gradient's ...

### Distinguishing text with opposite meanings in SVM (False Information Detection)

Going step by step: Preprocessing Preprocessing is a big deal in NLP, out there you'll find many tutorials describing the classic steps but few explanations about why and when you should actually ...
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### Does the bag-of-visual-words method improve the classification accuracy?

Bag-of-visual words (BOVW) was classicly used in computer vision before the introduction of neural networks or some more advanced classical techniques, such us VLAD or Fisher Vectors. In any case, it ...
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### How does an svm work? How does it perform comparisons between malignant and benign tumor

I will try to give you a simplified explanation of how SVMs work. The data one works with can be of two types. Either it is very easily separable and there is a clear straight line boundary between ...
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