✦ LIBER ✦

A probabilistic modeling of MOSAIC learning

✍ Scribed by Satoshi Osaga; Jun-ichiro Hirayama; Takashi Takenouchi; Shin Ishii

Publisher: Springer Japan
Year: 2008
Tongue: English
Weight: 449 KB
Volume: 12
Category: Article
ISSN: 1433-5298
DOI: 10.1007/s10015-007-0461-9

No coin nor oath required. For personal study only.

📜 SIMILAR VOLUMES

Learning probabilistic logic models from

Learning probabilistic logic models from probabilistic examples

✍ Jianzhong Chen; Stephen Muggleton; José Santos 📂 Article 📅 2008 🏛 Springer 🌐 English ⚖ 819 KB

A probabilistic model of learning by mea

A probabilistic model of learning by means of problems

✍ J. Brody 📂 Article 📅 1973 🏛 John Wiley and Sons 🌐 English ⚖ 236 KB

Learning probabilistic models of tree ed

Learning probabilistic models of tree edit distance

✍ Marc Bernard; Laurent Boyer; Amaury Habrard; Marc Sebban 📂 Article 📅 2008 🏛 Elsevier Science 🌐 English ⚖ 722 KB

Nowadays, there is a growing interest in machine learning and pattern recognition for tree-structured data. Trees actually provide a suitable structural representation to deal with complex tasks such as web information extraction, RNA secondary structure prediction, computer music, or conversion of

Probabilistic Modeling of Conformational

Probabilistic Modeling of Conformational Space for 3D Machine Learning Approaches

✍ Andreas Jahn; Georg Hinselmann; Nikolas Fechner; Carsten Henneges; Andreas Zell 📂 Article 📅 2010 🏛 Wiley (John Wiley & Sons) 🌐 English ⚖ 435 KB

## Abstract We present a new probabilistic encoding of the conformational space of a molecule that allows for the integration into common similarity calculations. The method uses distance profiles of flexible atom‐pairs and computes generative models that describe the distance distribution in the c

Learning first-order probabilistic model

Learning first-order probabilistic models with combining rules

✍ Sriraam Natarajan; Prasad Tadepalli; Thomas G. Dietterich; Alan Fern 📂 Article 📅 2008 🏛 Springer Netherlands 🌐 English ⚖ 620 KB

Evolutionary learning of dynamic probabi

Evolutionary learning of dynamic probabilistic models with large time lags

✍ Allan Tucker; Xiaohui Liu; Andrew Ogden-Swift 📂 Article 📅 2001 🏛 John Wiley and Sons 🌐 English ⚖ 322 KB

In this paper, we explore the automatic explanation of multivariate time series MTS Ž . through learning dynamic Bayesian networks DBNs . We have developed an evolutionary algorithm which exploits certain characteristics of MTS in order to generate good networks as quickly as possible. We compare th