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https://stats.stackexchange.com/questions/126709/svm-number-of-support-vectors
$\begingroup$ @MarcClaesen A large number of support vectors does not necessarily imply over-fitting. If you optimise the hyper-parameters using CV it is quite common to get a solution with a very bland kernel and a small value of C, in which case you end up with a lot of the data being support vectors, but a smooth model.
https://blog.statsbot.co/support-vector-machines-tutorial-c1618e635e93
Aug 15, 2017 · It’s time to catch up and introduce you to SVM without hard math and share useful libraries and resources to get you started. If you have used machine learning to perform classification, you might have heard about Support Vector Machines (SVM). Introduced a …Author: Abhishek Ghose
https://theprofessionalspoint.blogspot.com/2019/03/advantages-and-disadvantages-of-svm.html
Mar 01, 2019 · SVM (Support Vector Machine) classifies the data using hyperplane which acts like a decision boundary between different classes. Extreme data points from each class are called Support Vectors. SVM tries to find the best and optimal hyperplane which has maximum margin from each Support Vector.Location: Gurgaon, India
https://stackoverflow.com/questions/42150034/r-support-vector-machine-support-vectors-have-too-many-features
R support vector machine - support vectors have too many features. When I train my SVM the output support vectors have many more features than my input data. In the below example I use a small subset of the data (10 rows of 6 features to predict a binary class) but the support vectors …
https://www.quora.com/In-a-support-vector-machine-the-number-of-support-vectors-can-be-much-smaller-than-the-training-set-How-can-this-feature-be-useful
Apart from obvious, we can assume two things by looking at number of support vectors. Something about difficulty of the problem and amount of overfitting. Too many support vectors can tell us that we might be overfitting or the problem was actually difficult.
http://web.mit.edu/6.034/wwwbob/svm.pdf
•In general, lots of possible solutions for a,b,c (an infinite number!) •Support Vector Machine (SVM) finds an optimal solution. 4. Support Vector Machine (SVM) Support vectors Maximize margin. •SVMs maximize the margin (Winston terminology: the ‘street’) around the separating hyperplane.
https://scikit-learn.org/stable/modules/svm.html
Support Vector Machines are powerful tools, but their compute and storage requirements increase rapidly with the number of training vectors. The core of an SVM is a quadratic programming problem (QP), separating support vectors from the rest of the training data.
https://www.quora.com/Support-Vector-Machines-What-should-I-do-if-there-are-too-many-items-in-the-positive-result-after-predicting
Aug 24, 2012 · Simple Answer: You are trying to measure and improve not the accuracy but the precision of your prediction. The standard SVM formulation optimizes accuracy. To optimize precision, you can use SVM_Perf [1,2], which allows you to optimize multivariate performance measures like precision.
https://www.reddit.com/r/MachineLearning/comments/3bf44r/are_there_general_rules_that_tell_you_how_many/
Jun 28, 2015 · submitted 3 years ago by winstonl. I am using a support vector machine to predict a data set with 650 observations. There are roughly 15 covariates, and then end result is to classify whether the observations belong to group A or group B. I ran a SVM, and ended up just over 300 support vectors (315 I …
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