Cited by 577Domain Adaptation and Few-Shot LearningMachine Learning and AlgorithmsNeural Networks and Applications

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Multitask Learning

Caruana, Rich·1998·Learning to Learn

DOI 10.1007/978-1-4615-5529-2_5

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Learning representations by back-propagating errors
Rumelhart, D. E., Hinton, G. E., & Williams, R. J. (1986) · Nature · doi:10.1038/323533a0 · Cited by 31,892
Induction of decision trees
Quinlan, J. R. (1986) · Machine Learning · doi:10.1007/bf00116251 · Cited by 21,439
Backpropagation Applied to Handwritten Zip Code Recognition
LeCun, Y., Boser, B., Denker, J. S., Henderson, D., Howard, R. E., Hubbard, W., & Jackel, L. D. (1989) · Neural Computation · doi:10.1162/neco.1989.1.4.541 · Cited by 11,891
Proceedings of the 24th international conference on Machine learning
(2007) · doi:10.1145/1273496 · Cited by 11,869
Multitask Learning
Caruana, R. (1997) · Machine Learning · doi:10.1023/a:1007379606734 · Cited by 6,636
Self-citation
Statistical Analysis With Missing Data
Ghosh, S., Little, R. J. A., & Rubin, D. B. (1988) · Technometrics · doi:10.2307/1269814 · Cited by 4,730
Causation, Prediction, and Search
Spirtes, P., Glymour, C., & Scheines, R. (1993) · Lecture Notes in Statistics · doi:10.1007/978-1-4612-2748-9 · Cited by 4,617
A Bayesian Method for the Induction of Probabilistic Networks from Data
Cooper, G. F., & Herskovits, E. H. (1992) · Machine Learning · doi:10.1023/a:1022649401552 · Cited by 4,241
Hierarchical Mixtures of Experts and the EM Algorithm
Jordan, M. I., & Jacobs, R. A. (1994) · Neural Computation · doi:10.1162/neco.1994.6.2.181 · Cited by 2,786
Proceedings of the International Joint Conference on Artificial Intelligence 2007
Bundy, A. (2007) · International Joint Conference on Artificial Intelligence · Cited by 2,761

Show all 52 references

Research works printed but not linked: 7

Baxter, J., “Learning Internal Representations,” Ph.D. Thesis, The Flinders Univeristy of South Australia, Dec. 1994.
Caruana, R., “Multitask Connectionist Learning,” Proceedings of the 1993 Connectionist Models Summer School, 1994, pp. 372–379.
Caruana, R., Baluja, S., and Mitchell, T, “Using the Future to “Sort Out” the Present: Rankprop and Multitask Learning for Medical Risk Prediction,” Advances in Neural Information Processing Systems 8, (Proceedings of NIPS-95), 1996, pp. 959–965.
de Sa, V. R., “Learning Classification with Unlabelled Data,” Advances in Neural Information Processing Systems 6, (Proceedings of NIPS-93), 1994, pp. 112–119.
Martin, J. D., “Goal-directed Clustering,” Proceedings of the 1994 AAAI Spring Symposium on Goal-directed Learning, 1994.
O’Sullivan, J. and Thrun, S., “Discovering Structure in Multiple Learning Tasks: The TC Algorithm,” Proceedings of the 13th International Conference on Machine Learning, ICML-96, Bari, Italy, 1996, pp. 489–497.
Simard, P., Victorri, B., LeCun, Y, and Denker, J., “Tangent Prop—A Formalism for Specifying Selected Invariances in an Adaptive Neural Network,” Advances in Neural Information Processing Systems 4, (Proceedings of NIPS-91) 1992, pp. 895–903.

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Authors: Caruana, R.
Corresponding author: Caruana, Rich
ORCID: Caruana, Rich
Venue: Learning to Learn
Publisher: Springer US
Published: 1998
Pages: 95-133
DOI: 10.1007/978-1-4615-5529-2_5
OpenAlex: W4239943352
Cited by: 577
References: 52
Citations by year: 2026: 6 · 2025: 20 · 2024: 39 · 2023: 106 · 2022: 98 · 2021: 31 · 2020: 27 · 2019: 52 · 2018: 99 · 2017: 63 · 2016: 9 · 2015: 14 · 2014: 4 · 2013: 1 · 2012: 1
FWCI: 0.00
Type: book chapter
Language: en
Open access: closed
Locations: Springer eBooks (publishedVersion)
Topics: Physical Sciences › Computer Science › Artificial Intelligence › Domain Adaptation and Few-Shot Learning
Physical Sciences › Computer Science › Artificial Intelligence › Machine Learning and Algorithms
Physical Sciences › Computer Science › Artificial Intelligence › Neural Networks and Applications
Keywords: multi-task learning, inductive transfer, inductive bias, shared representations, task relatedness, k-nearest neighbors, kernel regression, decision tree, generalization

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