What is C SVC in LIBSVM?

LIBSVM is an integrated software for support vector classification, (C-SVC, nu-SVC), regression (epsilon-SVR, nu-SVR) and distribution estimation (one-class SVM). It supports multi-class classification. Since version 2.8, it implements an SMO-type algorithm proposed in this paper: R.-E.

What is LIBSVM format?

MLlib supports reading training examples stored in LIBSVM format, which is the default format used by LIBSVM and LIBLINEAR . It is a text format in which each line represents a labeled sparse feature vector using the following format: label index1:value1 index2:value2 …

What is LIBSVM algorithm?

LIBSVM implements the Sequential minimal optimization (SMO) algorithm for kernelized support vector machines (SVMs), supporting classification and regression. LIBLINEAR implements linear SVMs and logistic regression models trained using a coordinate descent algorithm.

What is the difference between SVC and SVM?

The limitation of SVC is compensated by SVM non-linearly. And that’s the difference between SVM and SVC. If the hyperplane classifies the dataset linearly then the algorithm we call it as SVC and the algorithm that separates the dataset by non-linear approach then we call it as SVM.

What is Gamma in SVC?

gamma. gamma is a parameter for non linear hyperplanes. The higher the gamma value it tries to exactly fit the training data set gammas = [0.1, 1, 10, 100]for gamma in gammas: svc = svm.SVC(kernel=’rbf’, gamma=gamma).fit(X, y)

How do I run LIBSVM in Matlab?

Here are the steps to run libsvm on MATLAB:

  1. Download it from here.
  2. Add the svmtrain, svmpredict, libsvmwrite & libscmread . mex Files to your matlab path (probably you just put them in the working folder…)

What is SMO in machine learning?

The new SVM learning algorithm is called Sequential Minimal Optimization (or SMO). Instead of previous SVM learning algorithms that use numerical quadratic programming (QP) as an inner loop, SMO uses an analytic QP step. This paper first provides an overview of SVMs and a review of current SVM training algorithms.

How do I run Libsvm in Matlab?

What is Rho in SVM?

rho is the bias term in the decision function sgn(w^Tx – rho). nSV and nBSV are number of support vectors and bounded support vectors (i.e., alpha_i = C). nu-svm is a somewhat equivalent form of C-SVM where C is replaced by nu. nu simply shows the corresponding parameter.

What is SVC and SVR?

SVC is a classifier, SVR is a regressor.

How can I get the decision values from LIBSVM?

You can obtain values by easily calling the subroutine svm_predict_values. Their corresponding labels can be obtained from svm_get_labels. Details are in README of libsvm package. If you are using MATLAB/OCTAVE interface, svmpredict can directly give you decision values. Please see matlab/README for details. We do not recommend the following.

Can I use non-numerical data in LIBSVM?

Currently libsvm supports only numerical data. You may have to change non-numerical data to numerical. For example, you can use several binary attributes to represent a categorical attribute. [Go Top]Q: Why do you consider sparse format? Will the training of dense data be much slower? This is a controversial issue.

Why is LIBSVM so hard to use?

In theory libsvm guarantees to converge. Therefore, this means you are handling ill-conditioned situations (e.g. too large/small parameters) so numerical difficulties occur. You may get better numerical stability by replacing typedef float Qfloat;

Is it possible to separate training/testing data in LIBSVM?

Each training/testing data must be in one line. It cannot be separated. In addition, you have to remove empty lines. [Go Top]Q: The program keeps running (with output, i.e. many dots). What should I do? In theory libsvm guarantees to converge.