Support vector machines (SVMs) are a set of related supervised learning methods that analyze data and recognize patterns, used for classification and regression analysis.
I am using the NuSVC class in sklearn. After trying to instantiate an NuSVC object as follows: self.classifier = OneVsRestClassifier(…
python scikit-learn svm svcI want to implement a simple SVM classifier, in the case of high-dimensional binary data (text), for which I think …
machine-learning svmDocumentation here. I'm wondering how important the coef0 parameter is for SVCs under the polynomial and sigmoid kernels. As I …
svm scikit-learn libsvm svcI'm trying to convert some old code from using sklearn to Keras implementation. Since it is crucial to maintain the …
machine-learning keras scikit-learn neural-network svmI am trying to implement the SVM loss function and its gradient. I found some example projects that implement these …
python machine-learning svm gradientI want to understand what is a vector in terms of machine learning. I looked into the below 2 links: https://…
vector machine-learning svm vectorizationAlmost all of the examples are based on numbers. In text documents i have words instead of numbers. So can …
svm knn document-classification text-classificationI'm trying to use the CVXOPT qp solver to compute the Lagrange Multipliers for a Support Vector Machine def svm(…
python svm typeerror cvxopt quadratic-programmingI have a training dataset of 1600000 tweets. How can I train this type of huge data. I have tried something …
python classification nltk svm naivebayesI'm new to R and I've ran into this problem: I want to compare two prediction techniques (Support Vector Machines …
r svm roc proc-r-package