Support Vector Machine Binary Classifier

A Support Vector Machine Binary Classifier is a Support Vector Machine Classifier that is a Binary Classifiers.

• See: Kernel Function.

---

---

References

2004

• (Hastie et al., 2004) ⇒ Trevor Hastie, Saharon Rosset, Robert Tibshirani, and Ji Zhu. (2004). “The Entire Regularization Path for the Support Vector Machine.” In: The Journal of Machine Learning Research, 5.
  • The support vector machine (SVM) is a widely used tool for classification. Many efficient implementations exist for fitting a two-class SVM model. The user has to supply values for the tuning parameters: the regularization cost parameter, and the kernel parameters.
  • In this paper we study the support vector machine (SVM)(Vapnik, 1996; Schölkopf and Smola, 2001) for two-class classification. We have a set of [math]\displaystyle{ n }[/math] training pairs x_i, y_i, where x_i in R^p is a p-vector of real-valued predictors (attributes) for the i^th observation, and y_i in {−1,+1} codes its binary response. We start off with the simple case of a linear classifier, where our goal is to estimate a linear decision function
  • ƒ(x) = β₀+β^Tx,
• and its associated classifier
  • Class(x) = sign[ƒ(x)].

---