The changing science of machine learning

Machine Learning

Volumn 82, Issue 3, 2011, Pages 275-279

  Langley, Pat ^a  

^a Arizona State University   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

COMPLEX TASK; DATA SETS; EMPIRICAL STUDIES; EXPERIMENTAL SCIENCE; LANGUAGE UNDERSTANDING; LOGICAL FORMULAS; ON-MACHINES; PRODUCTION RULES; RESEARCH EFFORTS; SYMBOLIC REPRESENTATION;

DECISION TREES; RESEARCH;

LEARNING SYSTEMS;

EID: 79959753092     PISSN: 08856125     EISSN: 15730565     Source Type: Journal    
DOI: 10.1007/s10994-011-5242-y     Document Type: Review

References (9)

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2. Kibler, D., & Langley, P. (1988). Machine learning as an experimental science. In Proceedings of the third European working session on learning (pp. 81-92). Glasgow: Pittman.
3. Laird, J. E., Rosenbloom, P. S., & Newell, A. (1986). Chunking in SOAR: The anatomy of a general learning mechanism. Machine Learning, 1, 11-46.
4. Langley, P. (1986). Human and machine learning. Machine Learning, 1, 243-248.
5. Langley, P. (1987). Research papers in machine learning. Machine Learning, 2, 195-198.
6. Langley, P. (1989). Toward a unified science of machine learning. Machine Learning, 3, 253-259.
7. Michalski, R. S., Carbonell, J. G., & Mitchell, T. M. (Eds.) (1983). Machine learning: An artificial intelligence approach. San Mateo: Morgan Kaufmann.
8. Richards, B. L., & Mooney, R. J. (1995). Automated refinement of first-order horn-clause domain theories. Machine Learning, 19, 95-131.
9. Simon, H. A. (1983). Why should machines learn? In R. S. Michalski, J. G. Carbonell, & T. M. Mitchell (Eds.), Machine learning: An artificial intelligence approach. San Mateo: Morgan Kaufmann.