Semantic Scholar Open Access 2007 2183 sitasi

Kernel methods in machine learning

Thomas Hofmann B. Scholkopf Alex Smola

Lihat Sumber DOI

Abstrak

We review machine learning methods employing positive definite kernels. These methods formulate learning and estimation problems in a reproducing kernel Hilbert space (RKHS) of functions defined on the data domain, expanded in terms of a kernel. Working in linear spaces of function has the benefit of facilitating the construction and analysis of learning algorithms while at the same time allowing large classes of functions. The latter include nonlinear functions as well as functions defined on nonvectorial data. We cover a wide range of methods, ranging from binary classifiers to sophisticated methods for estimation with structured data.

Topik & Kata Kunci

Mathematics

Penulis (3)

Thomas Hofmann

B. Scholkopf

Alex Smola

Format Sitasi

APA MLA BibTeX

Hofmann, T., Scholkopf, B., Smola, A. (2007). Kernel methods in machine learning. https://doi.org/10.1214/009053607000000677

Akses Cepat

Lihat di Sumber doi.org/10.1214/009053607000000677

Informasi Jurnal

Tahun Terbit: 2007
Bahasa: en
Total Sitasi: 2183×
Sumber Database: Semantic Scholar
DOI: 10.1214/009053607000000677
Akses: Open Access ✓