Λεπτομέρειες βιβλιογραφικής εγγραφής
| Τίτλος: |
Hypothesizing an algorithm from one example: the role of specificity. |
| Συγγραφείς: |
Muggleton FREng, S. H. |
| Πηγή: |
Philosophical Transactions of the Royal Society A: Mathematical, Physical & Engineering Sciences; 7/24/2023, Vol. 381 Issue 2251, p1-15, 15p |
| Θεματικοί όροι: |
Statistical learning, Artificial intelligence, Machine learning, Learning, Algorithms, Stereo vision (Computer science) |
| Περίληψη: |
Statistical machine learning usually achieves high-accuracy models by employing tens of thousands of examples. By contrast, both children and adult humans typically learn new concepts from either one or a small number of instances. The high data efficiency of human learning is not easily explained in terms of standard formal frameworks for machine learning, including Gold's learning-in-the-limit framework and Valiant's probably approximately correct (PAC) model. This paper explores ways in which this apparent disparity between human and machine learning can be reconciled by considering algorithms involving a preference for specificity combined with program minimality. It is shown how this can be efficiently enacted using hierarchical search based on identification of certificates and push-down automata to support hypothesizing compactly expressed maximal efficiency algorithms. Early results of a new system called DeepLog indicate that such approaches can support efficient top-down construction of relatively complex logic programs from a single example. This article is part of a discussion meeting issue 'Cognitive artificial intelligence'. [ABSTRACT FROM AUTHOR] |
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| Βάση Δεδομένων: |
Complementary Index |