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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 ...
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.
SVMs can be used to solve various real-world problems: SVMs are helpful in text and hypertext categorization, as their application can significantly reduce the need for labeled training instances in both the standard inductive and transductive settings. [11] Some methods for shallow semantic parsing are based on support vector machines. [12]
For many algorithms that solve these tasks, the data in raw representation have to be explicitly transformed into feature vector representations via a user-specified feature map: in contrast, kernel methods require only a user-specified kernel, i.e., a similarity function over all pairs of data points computed using inner products.
Whereas the SVM classifier supports binary classification, multiclass classification and regression, the structured SVM allows training of a classifier for general structured output labels. As an example, a sample instance might be a natural language sentence, and the output label is an annotated parse tree. Training a classifier consists of ...
The hyperplane learned in feature space by an SVM is an ellipse in the input space. In machine learning , the polynomial kernel is a kernel function commonly used with support vector machines (SVMs) and other kernelized models, that represents the similarity of vectors (training samples) in a feature space over polynomials of the original ...
Numerical methods for ordinary differential equations — the numerical solution of ordinary differential equations (ODEs) Euler method — the most basic method for solving an ODE; Explicit and implicit methods — implicit methods need to solve an equation at every step; Backward Euler method — implicit variant of the Euler method
A training data set is a data set of examples used during the learning process and is used to fit the parameters (e.g., weights) of, for example, a classifier. [9] [10]For classification tasks, a supervised learning algorithm looks at the training data set to determine, or learn, the optimal combinations of variables that will generate a good predictive model. [11]