Is SMO same as SVM?

The sequential minimal optimization algorithm (SMO) has been shown to be an effective method for training support vector machines (SVMs) on classification tasks defined on sparse data sets. SMO differs from most SVM algorithms in that it does not require a quadratic programming solver.

What is SMO in SVM?

Sequential minimal optimization (SMO) is an algorithm for solving the quadratic programming (QP) problem that arises during the training of support-vector machines (SVM). SMO is widely used for training support vector machines and is implemented by the popular LIBSVM tool.

What is SMO Weka?

SMO refers to the specific efficient optimization algorithm used inside the SVM implementation, which stands for Sequential Minimal Optimization. Weka Configuration for the Support Vector Machines Algorithm.

How can we implement SVM in Weka?

In Weka (GUI) go to Tools -> PackageManager and install LibSVM/LibLinear (both are SVM). Alternatively you can use . jar files of these algorithms and use through your java code.

What is IBk algorithm?

The IBk algorithm does not build a model, instead it generates a prediction for a test instance just-in-time. The IBk algorithm uses a distance measure to locate k “close” instances in the training data for each test instance and uses those selected instances to make a prediction.

What is sequential minimal optimization (SMO)?

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.

What is SSMO in SVM?

SMO is widely used for training support vector machines and is implemented by the popular LIBSVM tool. The publication of the SMO algorithm in 1998 has generated a lot of excitement in the SVM community, as previously available methods for SVM training were much more complex and required expensive third-party QP solvers.

What is the SMO algorithm?

The SMO algorithm is closely related to a family of optimization algorithms called Bregman methods or row-action methods. These methods solve convex programming problems with linear constraints. They are iterative methods where each step projects the current primal point onto each constraint.

What does SMO stand for?

Jump to navigation Jump to search. Sequential minimal optimization (SMO) is an algorithm for solving the quadratic programming (QP) problem that arises during the training of support vector machines. It was invented by John Platt in 1998 at Microsoft Research.