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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.
LIBSVM and LIBLINEAR are two popular open source machine learning libraries, both developed at the National Taiwan University and both written in C++ though with a C API. LIBSVM implements the sequential minimal optimization (SMO) algorithm for kernelized support vector machines (SVMs), supporting classification and regression. [1]
C. C Sharp (programming language) C Sharp 2.0; Comparison of C Sharp and Java; Comparison of C Sharp and Visual Basic .NET; C Sharp syntax; Callable object; Centripetal Catmull–Rom spline; Closure (computer programming) Code folding; Command pattern; Comparison of programming languages (list comprehension) Composite pattern; Composition over ...
SVM algorithms categorize binary data, with the goal of fitting the training set data in a way that minimizes the average of the hinge-loss function and L2 norm of the learned weights. This strategy avoids overfitting via Tikhonov regularization and in the L2 norm sense and also corresponds to minimizing the bias and variance of our estimator ...
Microsoft first used the name C# in 1988 for a variant of the C language designed for incremental compilation. [37] That project was not completed, and the name was later reused. C-sharp musical note. The name "C sharp" was inspired by the musical notation whereby a sharp symbol indicates that the written note should be made a semitone higher ...
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]
Space vector modulation (SVM) is an algorithm for the control of pulse-width modulation (PWM), invented by Gerhard Pfaff, Alois Weschta, and Albert Wick in 1982. [ 1 ] [ 2 ] It is used for the creation of alternating current (AC) waveforms ; most commonly to drive 3 phase AC powered motors at varying speeds from DC using multiple class-D ...
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 .