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Abstract: Machine learning's focus on ill-defined problems and highly flexible methods makes it ideally suited for KDD applications. Among the ideas machine learning contributes to KDD are the importance of empirical validation, the impossibility of learning without a priori assumptions, and the utility of limited-search or limited-representation methods. Machine learning provides methods for incorporating knowledge into the learning process, changing and combining representations, combatting the curse... (Update)
Active bibliography (related documents): More All
0.9: The Role of Occam's Razor in Knowledge Discovery - Domingos (1999) (Correct)
0.5: Mining Time-Changing Data Streams - Hulten, Spencer, Domingos (2001) (Correct)
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0.1: Knowledge Acquisition from Examples Via Multiple Models - Domingos (1997) (Correct)
BibTeX entry: (Update)
@misc{ domingos-machine,
author = "Pedro Domingos",
title = "E4 - Machine Learning",
url = "citeseer.ist.psu.edu/205450.html" }
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Documents on the same site (http://www.gia.ist.utl.pt/~pedrod/): More
Linear-Time Rule Induction - Domingos (Correct)
A Process-Oriented Heuristic for Model Selection - Pedro Domingos (1998) (Correct)
On the Optimality of the Simple Bayesian Classifier under.. - Domingos, Pazzani (1997) (Correct)
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