𝔖 Bobbio Scriptorium
✦   LIBER   ✦

A probabilistic model of learning by means of problems

✍ Scribed by J. Brody

Publisher
John Wiley and Sons
Year
1973
Tongue
English
Weight
236 KB
Volume
3
Category
Article
ISSN
0028-3045

No coin nor oath required. For personal study only.

πŸ“œ SIMILAR VOLUMES

Optimization of Taxonomic Keys by Means
Optimization of Taxonomic Keys by Means of Probabilistic Modelling
✍ Dr. A. V. Sviridov; Dr. D. Leuschner πŸ“‚ Article πŸ“… 1986 πŸ› John Wiley and Sons 🌐 English βš– 445 KB πŸ‘ 2 views

A t present biological systematics, i. e. clesaification and identification of organisms, is 8180 a subject of the employment of mathematical methods. The topic of this paper is identification. One of the most important methode of identification is that by means of keys. Therefore a short review on

Evolutionary learning of dynamic probabi
Evolutionary learning of dynamic probabilistic models with large time lags
✍ Allan Tucker; Xiaohui Liu; Andrew Ogden-Swift πŸ“‚ Article πŸ“… 2001 πŸ› John Wiley and Sons 🌐 English βš– 322 KB

In this paper, we explore the automatic explanation of multivariate time series MTS Ε½ . 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

Learning color-appearance models by mean
Learning color-appearance models by means of feed-forward neural networks
✍ P. Campadelli; C. Gangai; R. Schettini πŸ“‚ Article πŸ“… 1999 πŸ› John Wiley and Sons 🌐 English βš– 525 KB

Device-independent color imaging demands a reliable color-appearance model. We present a method for faithfully approximating color-appearance models by means of feed-forward neural networks trained with the error back-propagation algorithm. In particular, we present experimental evidence that in sev

Probabilistic Modeling of Conformational
Probabilistic Modeling of Conformational Space for 3D Machine Learning Approaches
✍ Andreas Jahn; Georg Hinselmann; Nikolas Fechner; Carsten Henneges; Andreas Zell πŸ“‚ Article πŸ“… 2010 πŸ› Wiley (John Wiley & Sons) 🌐 English βš– 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

The effects of learning, parameter tying
The effects of learning, parameter tying and model refinement for improving probabilistic tagging
✍ Y.-C. Lin; T.-H. Chiang; K.-Y. Su πŸ“‚ Article πŸ“… 1995 πŸ› Elsevier Science 🌐 English βš– 184 KB

To reduce the estimation error introduced by insufficient training data, the parameters of probabilistic models are usually smoothed by different techniques, such as Good-Turing smoothing and back-off smoothing. However, the discriminative power of the model cannot be significantly enhanced simply w