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Questions tagged [svm]

Support Vector Machines (SVM) are a popular supervised machine learning algorithm that can be used for classification or regression.

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How to draw Deep learning network architecture diagrams?

I have built my model. Now I want to draw the network architecture diagram for my research paper. Example is shown below:
Muhammad Ali
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SVM using scikit learn runs endlessly and never completes execution

I am trying to run SVR using scikit-learn (python) on a training dataset that has 595605 rows and 5 columns (features) while the test dataset has 397070 rows. The data has been pre-processed and regularized. I am able to successfully run the test…
tejaskhot
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Are Support Vector Machines still considered "state of the art" in their niche?

This question is in response to a comment I saw on another question. The comment was regarding the Machine Learning course syllabus on Coursera, and along the lines of "SVMs are not used so much nowadays". I have only just finished the relevant…
Neil Slater
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When to use Random Forest over SVM and vice versa?

When would one use Random Forest over SVM and vice versa? I understand that cross-validation and model comparison is an important aspect of choosing a model, but here I would like to learn more about rules of thumb and heuristics of the two…
Rohit
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How to calculate mAP for detection task for the PASCAL VOC Challenge?

How to calculate the mAP (mean Average Precision) for the detection task for the Pascal VOC leaderboards? There said - at page 11: Average Precision (AP). For the VOC2007 challenge, the interpolated average precision (Salton and Mcgill 1986) was…
Alex
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What kinds of learning problems are suitable for Support Vector Machines?

What are the hallmarks or properties that indicate that a certain learning problem can be tackled using support vector machines? In other words, what is it that, when you see a learning problem, makes you go "oh I should definitely use SVMs for…
Ragnar
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Can you explain the difference between SVC and LinearSVC in scikit-learn?

I've recently started learning to work with sklearn and have just come across this peculiar result. I used the digits dataset available in sklearn to try different models and estimation methods. When I tested a Support Vector Machine model on the…
metjush
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In SVM Algorithm, why vector w is orthogonal to the separating hyperplane?

I am a beginner on Machine Learning. In SVM, the separating hyperplane is defined as $y = w^T x + b$. Why we say vector $w$ orthogonal to the separating hyperplane?
Chong Zheng
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How to increase accuracy of classifiers?

I am using OpenCV letter_recog.cpp example to experiment on random trees and other classifiers. This example has implementations of six classifiers - random trees, boosting, MLP, kNN, naive Bayes and SVM. UCI letter recognition dataset with 20000…
Mika
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Choose binary classification algorithm

I have a binary classification problem: Approximately 1000 samples in training set 10 attributes, including binary, numeric and categorical Which algorithm is the best choice for this type of problem? By default I'm going to start with SVM…
IgorS
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Intuition for the regularization parameter in SVM

How does varying the regularization parameter in an SVM change the decision boundary for a non-separable dataset? A visual answer and/or some commentary on the limiting behaviors (for large and small regularization) would be very helpful.
ASX
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What happens when we train a linear SVM on non-linearly separable data?

What happens when we train a basic support vector machine (linear kernel and no soft-margin) on non-linearly separable data? The optimisation problem is not feasible, so what does the minimisation algorithm return?
SVM
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What's the relationship between an SVM and hinge loss?

My colleague and I are trying to wrap our heads around the difference between logistic regression and an SVM. Clearly they are optimizing different objective functions. Is an SVM as simple as saying it's a discriminative classifier that simply…
Simon
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The differences between SVM and Logistic Regression

I am reading about SVM and I've faced to the point that non-kernelized SVMs are nothing more than linear separators. Therefore, is the only difference between an SVM and logistic regression the criterium to choose the boundary? Apparently, SVM…
David Masip
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Consequence of Feature Scaling

I am currently using SVM and scaling my training features to the range of [0,1]. I first fit/transform my training set and then apply the same transformation to my testing set. For example: ### Configure transformation and apply to training set …
mike1886
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