Types of Cost in Inductive Concept Learning



Krogh, A., and Vedelsby, J. (1995). Neural network
ensembles, cross validation, and active learning,
Neural
Information Processing Systems 7,
pp. 231-238. MIT
Press.

Mingers, J. (1989). An empirical comparison of pruning
measures for decision tree induction.
Machine Learning,
4: 227-243.

Opitz, D.W., and Shavlik, J.W. (1997). Connectionist
theory refinement: Genetically searching the space of
network topologies.
Journal of Artificial Intelligence
Research
, 6: 177-209.

Nunez, M. (1988). Economic induction: A case study.
Proceedings of the Third European Working Session on
Learning, EWSL-88,
pp. 139-145. California: Morgan
Kaufmann.

Nunez, M. (1991). The use of background knowledge in
decision tree induction.
Machine Learning, 6, 231-250.

Pearl, J. (1988). Probabilistic Reasoning in Intelligent
Systems: Networks of Plausible Inference.
California:
Morgan Kaufmann.

Pipitone, F., De Jong, K.A., and Spears, W.M. (1991). An
artificial intelligence approach to analog systems
diagnosis. In
Testing and Diagnosis of Analog Circuits
and Systems,
Ruey-wen Liu, editor. New York: Van
Nostrand-Reinhold.

Provost, F.J., Jensen, D., and Oates, T. (1999). Efficient
progressive sampling. In
Proceedings of the Fifth
International Conference on Knowledge Discovery and
Data Mining, KDD-99.

Tan, M. (1991a). Cost-sensitive reinforcement learning
for adaptive classification and control.
Proceedings of
the Ninth National Conference on Artificial Intelligence
,
774-780. San Jose, CA: AAAI Press.

Tan, M. (1991b). Learning a cost-sensitive internal
representation for reinforcement learning.
Proceedings
of the Eighth International Workshop on Machine
Learning,
358-362. Evanston, IL: Morgan Kaufmann.

Tan, M. (1993). Cost-sensitive learning of classification
knowledge and its applications in robotics.
Machine
Learning,
13, 7-33.

Turney, P.D. (1995a). Cost-sensitive classification:
Empirical evaluation of a hybrid genetic decision tree
induction algorithm.
Journal of Artificial Intelligence
Research
, 2, 369-409.

Turney, P.D. (1995b). Low size-complexity inductive
logic programming: The East-West Challenge
considered as a problem in cost-sensitive classification.
In
Proceedings of the Fifth International Inductive
Logic Programming Workshop,
247-263.

Turney, P.D. (1995c). Bias and the quantification of
stability.
Machine Learning, 20: 23-33.

Turney, P.D., Schaffer, C., and Holte, R. (1995). Editors.
Proceedings of the IJCAI-95 Workshop on Data
Engineering for Inductive Learning.
Montreal, Canada.
(
http://www.iit.nrc.ca/DEIL/).

van Rijsbergen, C.J. (1979). Information Retrieval (2nd
edition), Butterworths, London.

van Someren, M.W., Torres, C., and Verdenius, F. (1997).
A systematic description of greedy optimisation
algorithms for cost sensitive generalisation.
Proceedings
of Intelligent Data Analysis 1997 (IDA97)
, Springer
Verlag, New York, pp. 247-258.

Verdenius, F. (1991). A method for inductive cost
optimization.
Proceedings of the Fifth European
Working Session on Learning, EWSL-91
, pp. 179-191.
New York: Springer-Verlag.



More intriguing information

1. Getting the practical teaching element right: A guide for literacy, numeracy and ESOL teacher educators
2. The Challenge of Urban Regeneration in Deprived European Neighbourhoods - a Partnership Approach
3. An Economic Analysis of Fresh Fruit and Vegetable Consumption: Implications for Overweight and Obesity among Higher- and Lower-Income Consumers
4. The name is absent
5. Feature type effects in semantic memory: An event related potentials study
6. A Computational Model of Children's Semantic Memory
7. Prevalence of exclusive breastfeeding and its determinants in first 6 months of life: A prospective study
8. Cryothermal Energy Ablation Of Cardiac Arrhythmias 2005: State Of The Art
9. The Effects of Reforming the Chinese Dual-Track Price System
10. Labour Market Institutions and the Personal Distribution of Income in the OECD