Pedro Domingos
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Abstract
Tapping into the "folk knowledge" needed to advance machine learning applications.
- Bauer, E. and Kohavi, R. An empirical comparison of voting classification algorithms: Bagging, boosting and variants. Machine Learning 36 (1999), 105--142. Google Scholar
Digital Library
- Bengio, Y. Learning deep architectures for AI. Foundations and Trends in Machine Learning 2, 1 (2009), 1--127. Google ScholarDigital Library
- Benjamini, Y. and Hochberg, Y. Controlling the false discovery rate: A practical and powerful approach to multiple testing. Journal of the Royal Statistical Society, Series B, 57 (1995), 289--300.Google ScholarCross Ref
- Bernardo, J.M. and Smith, A.F.M. Bayesian Theory. Wiley, NY, 1994.Google ScholarCross Ref
- Blumer, A., Ehrenfeucht, A., Haussler, D. and Warmuth, M.K. Occam's razor. Information Processing Letters 24 (1987), 377--380.Google ScholarDigital Library
- Cohen, W.W. Grammatically biased learning: Learning logic programs using an explicit antecedent description language. Artificial Intelligence 68 (1994), 303--366.Google ScholarDigital Library
- Domingos, P. The role of Occam's razor in knowledge discovery. Data Mining and Knowledge Discovery 3 (1999), 409--425. Google ScholarDigital Library
- Domingos, P. Bayesian averaging of classifiers and the overfitting problem. In Proceedings of the 17
th International Conference on Machine Learning (Stanford, CA, 2000), Morgan Kaufmann, San Mateo, CA, 223--230. Google ScholarDigital Library
- Domingos, P. A unified bias-variance decomposition and its applications. In Proceedings of the 17
th International Conference on Machine Learning (Stanford, CA, 2000), Morgan Kaufmann, San Mateo, CA, 231--238. Google ScholarDigital Library
- Domingos, P. and Pazzani, M. On the optimality of the simple Bayesian classifier under zero-one loss. Machine Learning 29 (1997), 103--130. Google ScholarDigital Library
- Hulten, G. and Domingos, P. Mining complex models from arbitrarily large databases in constant time. In Proceedings of the 8
th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (Edmonton, Canada, 2002). ACM Press, NY, 525--531. Google ScholarDigital Library
- Kibler, D. and Langley, P. Machine learning as an experimental science. In Proceedings of the 3
rd European Working Session on Learning (London, UK, 1988). Pitman.Google Scholar
- Klockars, A.J. and Sax, G. Multiple Comparisons. Sage, Beverly Hills, CA, 1986.Google Scholar
- Kohavi, R., Longbotham, R., Sommerfield, D. and Henne, R. Controlled experiments on the Web: Survey and practical guide. Data Mining and Knowledge Discovery 18 (2009), 140--181. Google ScholarDigital Library
- Manyika, J., Chui, M., Brown, B., Bughin, J., Dobbs, R., Roxburgh, C. and Byers, A. Big data: The next frontier for innovation, competition, and productivity. Technical report, McKinsey Global Institute, 2011.Google Scholar
- Mitchell, T.M. Machine Learning. McGraw-Hill, NY, 1997. Google ScholarDigital Library
- Ng, A.Y. Preventing "overfitting" of cross-validation data. In Proceedings of the 14
th International Conference on Machine Learning (Nashville, TN, 1997). Morgan Kaufmann, San Mateo, CA, 245--253. Google ScholarDigital Library
- Pearl, J. On the connection between the complexity and credibility of inferred models. International Journal of General Systems 4 (1978), 255--264.Google ScholarCross Ref
- Pearl, J. Causality: Models, Reasoning, and Inference. Cambridge University Press, Cambridge, UK, 2000. Google ScholarDigital Library
- Quinlan, J.R. C4.5: Programs for Machine Learning. Morgan Kaufmann, San Mateo, CA, 1993.Google ScholarDigital Library
- Richardson, M. and P. Domingos. Markov logic networks. Machine Learning 62 (2006), 107--136. Google ScholarDigital Library
- Tenenbaum, J., Silva, V. and Langford, J. A global geometric framework for nonlinear dimensionality reduction. Science 290 (2000), 2319--2323.Google ScholarCross Ref
- Vapnik, V.N. The Nature of Statistical Learning Theory. Springer, NY, 1995. Google ScholarCross Ref
- Witten, I., Frank, E. and Hall, M. Data Mining: Practical Machine Learning Tools and Techniques, 3rd Edition. Morgan Kaufmann, San Mateo, CA, 2011. Google ScholarDigital Library
- Wolpert, D. The lack of a priori distinctions between learning algorithms. Neural Computation 8 (1996), 1341--1390. Google ScholarDigital Library
Index Terms
- A few useful things to know about machine learning
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