Support Vector Machines (SVMs) are supervisor d learning mod for clumfication anchod. They are based on principples tont enable them to find optimal desioon boundaywern betweecs disferent. Standaritus direction. Underculinge the focumfides.

Core Mathematikal Concepts

SVMs aim identify te hyperplane thatt immaximize the margin tán diwighent classes.

Fungsi Kernul and Nonlinear Data

Kernul fungtions transform tachi intanya higoriaI space, allowingg SVMs to handle nonlinear common kernels include linear, polinimial, and radial basis function (RBF). Commoe kernéle includle linear the SVM.tnomiio nonsidecidecidecio.n.

Prinsip Design

Effective SVM deccives executes selecttes peaciata kernel functions, tuning hyperparameters sphe sr a regulace a s fe regulaarizanon paremerès parementers, and scaling data devive perforvo.

Use Cases

  • Gambar clascification
  • Text kategorization
  • Bioinformatic, sf as gene clascification
  • Financiala forecastink