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We have seen the Minimum Message Length (MML) and other treatments
of "simple" hypotheses over simple data such as finite choices,
e.g., multi-state,
and continuous values,
e.g., normal distribution, etc..
It turns out that structured hypotheses
can be formed from combinations of simple hypotheses
plus a component to describe the particular structure.
Structured Models:
- Sequences, time-series, grids, images, ...
- Hidden Markov Models.
- HMM & PFSA.
- Segmentation.
- Bioinformatics, DNA, proteins, genes, evolutionary trees.
- Alignment and
- Phylogenetic (Evolutionary) Trees.
- Supervised Learning,
expert systems, regressions, rule learning, ...
- Decision-Trees (Classification-Trees),
supervised classification, and
- Decision-Graphs.
- Bayesian Networks, Causal Models, Graphical Models, ...
- Mixed Bayes Nets --
discrete & continuous (& structured) variables.
- Hybrid Models (local structure).
- Log-linear analysis,
Chordalysis MML
- ANNs, Artificial Neural Networks.
- Unsupervised Learning,
clustering, Snob, numerical taxonomy, rule discovery.
- Mixture Models,
unsupervised classification, clustering,
- in series,
- Stats. & Comp..
- Factor Analysis Models,
- Single Factor, &
- Multiple Factors.
- WB68, WF87.
- Inductive Programming (IP)
- ACSC03.
- II 1.0.
- JFP.
- ACSC06.
- ActaOe.
- IP 1.2, TR 224.
- Trees & Graphs.
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