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L. Schuchman, “Dither signals and their effect on quantization noise,” IEEE Transactions on Communication Technology , vol. 12, no. 4, pp. 162–165, 1964
1964
Earlier work this paper cites.
S. Zaks, “Lexicographic generation of ordered trees,” Theoretical Computer Science , vol. 10, no. 1, pp. 63–82, 1980
1980
Earlier work this paper cites.
S. P. Lloyd, “Least squares quantization in pcm,” IEEE Transactions on Information Theory , vol. 28, no. 2, pp. 129–137, 1982
1982
Earlier work this paper cites.
L. Breiman, J. Friedman, R. A. Olshen, and C. J. Stone, Classification and regression trees . CRC press, 1984
1984
Earlier work this paper cites.
J. Katajainen and E. Mäkinen, “Tree compression and optimization with applications,” International Journal of Foundations of Computer Science , vol. 1, no. 04, pp. 425–447, 1990
1990
Earlier work this paper cites.
J. R. Quinlan, C4.5: programs for machine learning . Morgan Kaufmann, 1992
1992
Earlier work this paper cites.
L. Breiman, “Bagging predictors,” Machine learning , vol. 24, no. 2, pp. 123–140, 1996
1996
Earlier work this paper cites.
L. P. Deutsch, “Gzip file format specification version 4.3,” 1996
1996
Earlier work this paper cites.
S. Chen and J. H. Reif, “Efficient lossless compression of trees and graphs,” in Data Compression Conference . Citeseer, 1996
1996
Earlier work this paper cites.
P. Geurts, “Some enhancements of decision tree bagging,” in Principles of Data Mining and Knowledge Discovery . Springer, 2000, pp. 136–147
2000
Earlier work this paper cites.
L. Breiman, “Random forests,” Machine learning , vol. 45, no. 1, pp. 5–32, 2001
2001
Earlier work this paper cites.
R. E. Schapire, “The boosting approach to machine learning: An overview,” in Nonlinear estimation and classification . Springer, 2003, pp. 149–171
2003
Earlier work this paper cites.
D. Tikk, L. T. Kóczy, and T. D. Gedeon, “A survey on universal approximation and its limits in soft computing techniques,” International Journal of Approximate Reasoning , vol. 33, no. 2, pp. 185–202, 2003
2003
Earlier work this paper cites.
R. Quinlan, “Data mining tools see5 and c5.0,” 2004
2004
Cited alongside, same era.
A. Orlitsky, N. P. Santhanam, and J. Zhang, “Universal compression of memoryless sources over unknown alphabets,” IEEE Transactions on Information Theory , vol. 50, no. 7, pp. 1469–1481, 2004
2004
Cited alongside, same era.
A. Banerjee, S. Merugu, I. S. Dhillon, and J. Ghosh, “Clustering with bregman divergences,” The Journal of Machine Learning Research , vol. 6, pp. 1705–1749, 2005
2005
Cited alongside, same era.
C. Bucilua, R. Caruana, and A. Niculescu-Mizil, “Model compression,” in Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining . ACM, 2006, pp. 535–541
2006
Cited alongside, same era.
T. Hothorn, K. Hornik, and A. Zeileis, “Unbiased recursive partitioning: A conditional inference framework,” Journal of Computational and Graphical statistics , vol. 15, no. 3, pp. 651–674, 2006
W. Szpankowski and M. J. Weinberger, “Minimax pointwise redundancy for memoryless models over large alphabets,” IEEE Transactions on Information Theory , vol. 58, no. 7, pp. 4094–4104, 2012
2012
Later among the works it cites.
T. M. Cover and J. A. Thomas, Elements of information theory . John Wiley & Sons, 2012
2012
Later among the works it cites.
A. Painsky, S. Rosset, and M. Feder, “Universal compression of memoryless sources over large alphabets via independent component analysis,” in Data Compression Conference (DCC), 2015 . IEEE, 2015, pp. 213–222
2015
Later among the works it cites.
A. Painsky and S. Rosset, “Compressing random forests,” in Data Mining (ICDM), 2016 IEEE 16th International Conference on . IEEE, 2016, pp. 1131–1136
2016
Later among the works it cites.
——, “A simple and efficient approach for adaptive entropy coding over large alphabets,” in Data Compression Conference (DCC), 2016 . IEEE, 2016, pp. 369–378
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2006
Cited alongside, same era.
P. Geurts, D. Ernst, and L. Wehenkel, “Extremely randomized trees,” Machine learning , vol. 63, no. 1, pp. 3–42, 2006
2006
Cited alongside, same era.
J. H. Friedman and B. E. Popescu, “Predictive learning via rule ensembles,” The Annals of Applied Statistics , pp. 916–954, 2008
2008
Cited alongside, same era.
F. T. Liu, K. M. Ting, Y. Yu, and Z.-H. Zhou, “Spectrum of variable-random trees,” Journal of Artificial Intelligence Research , vol. 32, pp. 355–384, 2008
2008
Cited alongside, same era.
T. Hastie, R. Tibshirani, and J. Friedman, The elements of statistical learning: data mining, inference and prediction , 2nd ed. Springer, 2009
2009
Cited alongside, same era.
S. Bernard, L. Heutte, and S. Adam, “On the selection of decision trees in random forests,” in International Joint Conference on Neural Networks . IEEE, 2009, pp. 302–307
2009
Cited alongside, same era.
N. Meinshausen, “Node harvest,” The Annals of Applied Statistics , pp. 2049–2072, 2010
2010
Cited alongside, same era.
A. Joly, F. Schnitzler, P. Geurts, and L. Wehenkel, “L1-based compression of random forest models,” in European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning , 2012
2012
Cited alongside, same era.
2016
Later among the works it cites.
——, “Generalized independent component analysis over finite alphabets,” IEEE Transactions on Information Theory , vol. 62, no. 2, pp. 1038–1053, 2016
2016
Later among the works it cites.
A. Painsky and S. Rosset, “Cross-validated variable selection in tree-based methods improves predictive performance,” IEEE transactions on pattern analysis and machine intelligence , vol. 39, no. 11, pp. 2142–2153, 2017
2017
Later among the works it cites.
——, “Large alphabet source coding using independent component analysis,” IEEE Transactions on Information Theory , vol. 63, no. 10, pp. 6514–6529, 2017
2017
Later among the works it cites.
2017
Later among the works it cites.
2018
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2018
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2018
Closest in time.
A. Painsky, S. Rosset, and M. G. Feder, “Linear independent component analysis over finite fields: Algorithms and bounds,” IEEE Transactions on Signal Processing , 2018
2018
Closest in time.