other | inductive programming |
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Weka's Bayesian networks "assume that all variables are discrete"[Weka] p.22 and "a limitation of the current classes is that they assume that there are no missing values"[Weka] p.23.
In Weka, continuous variables must be discretised first and the way this is done may affect the outcome. This is unnecessary for modelling and, for splitting, is part of the network optimisation when using our [IP] classification trees.
[Weka] R. R. Bouckaert. Bayesian networks in Weka. TR 14/2004, Comp. Sci. Dept.. U. of Waikato, Sept. 2004.
[IP] L. Allison.
Types and classes of machine learning and data mining.
as |
this | is to | that | |
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subject of this talk | inductive inference | (arbitrary types of data) | ||
list prelude | list processing | (arbitrary element types) | ||
{parser combinator} | parsing | (chars, strings, symbols, parse trees) | ||
embedded language |
X | |||
denotational semantics |
L | |||
?inductive programming? | statistical model | |||
functional programming | function |