Learning with the Minimum Description Length Principle

Kenji Yamanishi

Informasi Umum

Kode

24.21.1753

Klasifikasi

621.39 - Computer engineering, Electronic digital computers, Central processing units, Computer reliability, General Computer Performance Evaluation

Jenis

Buku - Elektronik (E-Book)

Subjek

Computer Engineering

No. Rak

Tel-U Gedung Manterawu Lantai 5 : Rak 13b
Tel-U Purwokerto : Rak 6

Dilihat

34 kali

Informasi Lainnya

Abstraksi

This book introduces readers to the minimum description length (MDL) principle and its applications in learning. The MDL is a fundamental principle for inductive inference, which is used in many applications including statistical modeling, pattern recognition and machine learning. At its core, the MDL is based on the premise that “the shortest code length leads to the best strategy for learning anything from data.” The MDL provides a broad and unifying view of statistical inferences such as estimation, prediction and testing and, of course, machine learning. The content covers the theoretical foundations of the MDL and broad practical areas such as detecting changes and anomalies, problems involving latent variable models, and high dimensional statistical inference, among others. The book offers an easy-to-follow guide to the MDL principle, together with other information criteria, explaining the differences between their standpoints.