Learning first-order probabilistic models with combining rules
β Scribed by Sriraam Natarajan; Prasad Tadepalli; Thomas G. Dietterich; Alan Fern
- Publisher
- Springer Netherlands
- Year
- 2008
- Tongue
- English
- Weight
- 620 KB
- Volume
- 54
- Category
- Article
- ISSN
- 1012-2443
No coin nor oath required. For personal study only.
π SIMILAR VOLUMES
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
## Abstract In this work we focus on iterative learning control (ILC) for iteratively varying reference trajectories, which are described by a highβorder internal models (HOIM) that can be formulated as a polynomials between two consecutive iterations. The classical ILC with iteratively invariant r