✦ LIBER ✦

Monotonicity maintenance in information-theoretic machine learning algorithms

✍ Scribed by Ben-David, Arie

Book ID: 118772157
Publisher: Springer
Year: 1995
Tongue: English
Weight: 688 KB
Volume: 19
Category: Article
ISSN: 0885-6125
DOI: 10.1007/bf00994659

No coin nor oath required. For personal study only.

📜 SIMILAR VOLUMES

Unifying cost and information in informa

Unifying cost and information in information-theoretic competitive learning

✍ Ryotaro Kamimura 📂 Article 📅 2005 🏛 Elsevier Science 🌐 English ⚖ 142 KB

New theoretical directions in machine le

New theoretical directions in machine learning

✍ Haussler, David 📂 Article 📅 1988 🏛 Springer 🌐 English ⚖ 204 KB

New Theoretical Directions In Machine Le

New Theoretical Directions In Machine Learning

✍ Haussler, David 📂 Article 📅 1988 🏛 Springer 🌐 English ⚖ 232 KB

Theoretical study of mean-field Boltzman

Theoretical study of mean-field Boltzmann machine learning by information geometry

✍ Toshiyuki Arai; Toshiyuki Tanaka; Yoritaka Fujimori 📂 Article 📅 1999 🏛 John Wiley and Sons 🌐 English ⚖ 187 KB

Mean-field Boltzmann machine learning is recognized as a practical method to circumvent the difficulty that Boltzmann machine learning is very time-consuming. However, its theoretical meaning is still not clear. In this paper, based on information geometry, we give an information-theoretic interpret

Machine Learning for Information Extract

Machine Learning for Information Extraction in Informal Domains

✍ Dayne Freitag 📂 Article 📅 2000 🏛 Springer 🌐 English ⚖ 170 KB

Paradigms in Measure Theoretic Learning

Paradigms in Measure Theoretic Learning and in Informant Learning

✍ Franco Montagna; Giulia Simi 📂 Article 📅 1999 🏛 Springer Netherlands 🌐 English ⚖ 814 KB