Database Reference
In-Depth Information
Kusiak A., Rough set theory: A data mining tool for semiconductor manufactur-
ing,
IEEE Transactions on Electronics Packaging Manufacturing
24(1):44-
50, 2001a.
Kusiak A., Feature transformation methods in data mining,
IEEE Transactions
on Elctronics Packaging Manufacturing
24(3):214-221, 2001b.
Kusiak A., and Kurasek C., Data mining of printed-circuit board defects,
IEEE
Transactions on Robotics and Automation
17(2):191-196, 2001.
Kusiak A., E. Szczerbicki, and K. Park, A novel approach to decomposition
of design specifications and search for solutions,
International Journal of
Production Research
29(7):1391-1406, 1991.
Langdon W. B., Barrett S. J., and Buxton B. F., Combining decision trees and
neural networks for drug discovery, In
Genetic Programming, Proceedings
of the 5th European Conference
, EuroGP 2002, Kinsale, Ireland, pp. 60-70,
2002.
Langley P., Selection of relevant features in machine learning, In
Proceedings of
the AAAI Fall Symposium on Relevance
, pp. 140-144, Portland, OR: AAAI
Press, 1994.
Langley P., and Sage S., Oblivious decision trees and abstract cases, In
Working
Notes of the AAAI-94 Workshop on Case-Based Reasoning
, pp. 113-117,
Seattle, WA: AAAI Press, 1994a.
Langley P., and Sage S., Induction of selective Bayesian classifiers, In
Proceedings
of the Tenth Conference on Uncertainty in Artificial Intelligence
, pp. 399-
406. Seattle, WA: Morgan Kaufmann, 1994b.
Larsen B., and Aone C., Fast and effective text mining using linear-time document
clustering, In
Proceedings of the 5th ACM SIGKDD
, pp. 16-22, San Diego,
CA, 1999.
Lee S., Noisy Replication in skewed binary classification,
Computational Statistics
and Data Analysis
, 34, 2000.
Leigh W., Purvis R., and Ragusa J. M., Forecasting the NYSE composite index
with technical analysis, pattern recognizer, neural networks, and genetic
algorithm: A case study in romantic decision support,
Decision Support
Systems
32(4):361-377, 2002.
Lewis D., and Catlett J., Heterogeneous uncertainty sampling for supervised
learning, In
Machine Learning: Proceedings of the Eleventh Annual Con-
ference
, pp. 148-156, New Brunswick, New Jersey: Morgan Kaufmann,
1994.
Lewis D., and Gale W., Training text classifiers by uncertainty sampling, In
Seventeenth Annual International ACM SIGIR Conference on Research and
Development in Information Retrieval
, pp. 3-12, 1994.
Li X., and Dubes R. C., Tree classifier design with a Permutation statistic,
Pattern
Recognition
19:229-235, 1986.
Liao Y., and Moody J., Constructing heterogeneous committees via input feature
grouping, In
Advances in Neural Information Processing Systems
,S.A.
Solla, T. K. Leen and K.-R. Muller (eds.), Vol.12, MIT Press, 2000.
Liaw A., and Wiener M., Classification and regression by random forest,
Rnews
2(3):18-22, 2002.
Search WWH ::
Custom Search