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The SVM learning code from both libraries is often reused in other open source machine learning toolkits, including GATE, KNIME, Orange [3] and scikit-learn. [4] Bindings and ports exist for programming languages such as Java, MATLAB, R, Julia, and Python. It is available in e1071 library in R and scikit-learn in Python.
Repository model, how working and shared source code is handled Shared, all developers use the same file system Client–server , users access a master repository server via a client ; typically, a client machine holds only a working copy of a project tree; changes in one working copy are committed to the master repository before becoming ...
Sequential minimal optimization (SMO) is an algorithm for solving the quadratic programming (QP) problem that arises during the training of support-vector machines (SVM). It was invented by John Platt in 1998 at Microsoft Research. [1] SMO is widely used for training support vector machines and is implemented by the popular LIBSVM tool.
A CVS server stores the modules it manages in its repository. Programmers acquire copies of modules by checking out. The checked-out files serve as a working copy, sandbox or workspace. Changes to the working copy are reflected in the repository by committing them. To update is to acquire or merge the changes in the repository with the working ...
The structured support-vector machine is a machine learning algorithm that generalizes the Support-Vector Machine (SVM) classifier. Whereas the SVM classifier supports binary classification, multiclass classification and regression, the structured SVM allows training of a classifier for general structured output labels.
The Bean Scripting Framework is a method of allowing the use of scripting in Java code. It provides a set of Java classes which provides support within Java applications for scripting languages, and also allows access to Java objects and methods. Some examples of languages that can be used in combination with BSF and Java include Python, Jython ...
In machine learning, kernel machines are a class of algorithms for pattern analysis, whose best known member is the support-vector machine (SVM). These methods involve using linear classifiers to solve nonlinear problems. [1]
Within software engineering, the mining software repositories [1] (MSR) field [2] analyzes the rich data available in software repositories, such as version control repositories, mailing list archives, bug tracking systems, issue tracking systems, etc. to uncover interesting and actionable information about software systems, projects and software engineering.