‘Memo’ Functions and Machine Learning¶
MICHIE, D. (1968). ‘Memo’ Functions and Machine Learning. Nature, 19-22.
Cited by¶
2 citations across 2 artifacts.
Each citation links to the sentence it supports in the citing article.
Mechanisms¶
- Cached Result Replay
- It is, in effect, memoization applied to an effectful operation — caching the outcome so a repeated call is a lookup, not a recomputation.
This sourceMichie's memo functions store computed values in a rote lookup table so later evaluations can use lookup instead of repeating the rule computation.
- It is, in effect, memoization applied to an effectful operation — caching the outcome so a repeated call is a lookup, not a recomputation.
- Resolved Value Memoization
- Its strength is eliminating redundant fulfillment: an expensive value is produced once and thereafter is a lookup, which is simply memoization
This sourceIntroduces memo functions that retain computed results so repeated requests can reuse them rather than recompute them.
- Its strength is eliminating redundant fulfillment: an expensive value is produced once and thereafter is a lookup, which is simply memoization
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Registry ID ref:4e3cec80d5f1 · see in the full table