The DOI matches its Crossref record; 3 of the 3 references it cites were found in a registry or an index.
Active Learning with Statistical Models
Open accessAbstract
For many types of machine learning algorithms, one can compute the statistically `optimal' way to select training data. In this paper, we review how optimal data selection techniques have been used with feedforward neural networks. We then show how the same principles may be used to select data for two alternative, statistically-based learning architectures: mixtures of Gaussians and locally weighted regression. While the techniques for neural networks are computationally expensive and approximate, the techniques for mixtures of Gaussians and locally weighted regression are both efficient and accurate. Empirically, we observe that the optimality criterion sharply decreases the number of training examples the learner needs in order to achieve good performance.
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Cites
Maximum Likelihood from Incomplete Data Via the EM Algorithm
Dempster, A. P., Laird, N. M., & Rubin, D. B. (1977) · Journal of the Royal Statistical Society Series B: Statistical Methodology · doi:10.1111/j.2517-6161.1977.tb01600.x · Cited by 50,444
Neural Networks and the Bias/Variance Dilemma
Geman, S., Bienenstock, E., & Doursat, R. (1992) · Neural Computation · doi:10.1162/neco.1992.4.1.1 · Cited by 3,578 · cited 1×
“The expectation inside the integral may be decomposed as follows (Geman, Bienenstock, & Doursat, 1992): (2) where ED[(cid:1)] denotes the expectation over training sets D and the remaining expectations on the right-hand side are expectations with respect to the conditional density P(yjx).”
BackgroundLearning Bayesian Networks: The Combination of Knowledge and Statistical Data
Heckerman, D., Geiger, D., & Chickering, D. M. (1994) · Uncertainty Proceedings 1994 · doi:10.1016/b978-1-55860-332-5.50042-0 · Cited by 261
Cited by
Bengio, Y., Louradour, J., Collobert, R., & Weston, J. (2009) · Proceedings of the 26th Annual International Conference on Machine Learning · doi:10.1145/1553374.1553380 · Cited by 5,366
Intrinsic Motivation Systems for Autonomous Mental Development
Oudeyer, P.-Y., Kaplan, F., & Hafner, V. V. (2007) · IEEE Transactions on Evolutionary Computation · doi:10.1109/tevc.2006.890271 · Cited by 1,126
On Learning, Representing, and Generalizing a Task in a Humanoid Robot
Calinon, S., Guenter, F., & Billard, A. (2007) · IEEE Transactions on Systems, Man and Cybernetics, Part B (Cybernetics) · doi:10.1109/tsmcb.2006.886952 · Cited by 1,090
Interactive machine learning for health informatics: when do we need the human-in-the-loop?
Holzinger, A. (2016) · Brain Informatics · doi:10.1007/s40708-016-0042-6 · Cited by 906
Information-seeking, curiosity, and attention: computational and neural mechanisms
Gottlieb, J., Oudeyer, P.-Y., Lopes, M., & Baranes, A. (2013) · Trends in Cognitive Sciences · doi:10.1016/j.tics.2013.09.001 · Cited by 784
Metadata
- Authors
- Cohn, D. A., Ghahramani, Z., & Jordan, M. I.
- Venue
- Journal of Artificial Intelligence Research
- Publisher
- AI Access Foundation
- Published
- 1996-03-01
- Volume
- 4
- Pages
- 129-145
- ISSN
- 1076-9757 (ISSN-L)
- OpenAlex
- W2949071206
- Cited by
- 1,317
- References
- 3
- FWCI
- 5.29
- Type
- journal article
- Language
- en
- Open access
- diamond
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Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
