An introduction to boosting and leveraging

An introduction to boosting and leveraging (2003)

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by Ron Meir , Gunnar Rätsch

Venue:	Advanced Lectures on Machine Learning, LNCS
Citations:	78 - 8 self

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@INPROCEEDINGS{Meir03anintroduction,
    author = {Ron Meir and Gunnar Rätsch},
    title = {An introduction to boosting and leveraging},
    booktitle = {Advanced Lectures on Machine Learning, LNCS},
    year = {2003},
    pages = {119--184},
    publisher = {Springer}
}

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6694	Statistical Learning Theory - Vapnik - 1998
4071	C4.5: Programs for machine learning - Quinlan - 1993
3909	Neural Networks for Pattern Recognition - Bishop - 1995
3143	Classification and Regression Trees - Breiman, Friedman, et al. - 1983
2715	Neural networks: A comprehensive foundation (2nd ed.). Upper Saddle River - Haykin - 1999
1998	Bagging predictors - Breiman - 1996
1714	Schapire R: A decision-theoretic generalization of online learning and an application to boosting - Freund - 1997
1515	A Theory of the Learnable - Valiant - 1984
1511	Online learning with kernels - Kivinen, Smola, et al.
1491	Support-vector networks - Cortes, Vapnik - 1995
1420	An Introduction to Probability Theory and Its Applications - Feller - 1968
1325	Experiments with a new boosting algorithm - Freund, Schapire - 1996
1131	A new look at the statistical model identification - Akaike - 1974
1089	Atomic decomposition by basis pursuit - Chen, Donoho, et al. - 1998
933	A training algorithm for optimal margin classifiers - Boser, Guyon, et al. - 1992
896	Additive logistic regression: a statistical view of boosting - Friedman, Hastie, et al. - 2000
891	Modeling by shortest data description - Rissanen - 1978
816	On the uniform convergence of relative frequencies of events to their probabilities. Theory of Prob. and its - Vapnik, Chervonenkis - 1971
801	A probabilistic theory of pattern recognition - Devroye, Györfi, et al. - 1996
741	Shawe-Taylor J (2000) An Introduction to Support Vector Machines and other kernel-based learning methods - Cristianini
699	Matching Pursuits with Time-Frequency Dictionaries - 27Mallat, Zhang - 1993
687	Nearest neighbor pattern classification - Cover, Hart - 1967
634	Hierarchical mixtures of experts and the EM algorithm - Jordan, Jacobs - 1994
606	W: Boosting the Margin: A New Explanation for the Effectiveness of Voting Methods. The Annals of Statistics - Schapire, Freund, et al. - 1998
593	Solution of IllPosed problems - Tikhonov, Arsenin - 1977
561	Y: Improved boosting algorithms using confidence-rated predictions - RE, Singer - 1999
554	Nonlinear Programming. Athena Scientific - Bertsekas - 1995
554	The strenght of weak learnability - Schapire - 1990
507	An Introduction to Computational Learning Theory - Kearns, Vazirani - 1994
464	Inducing Features of Random Fields - Pietra, Pietra, et al. - 1997
449	Kohavi R: An empirical comparison of voting classification algorithms - Bauer - 1999
448	Solving Multiclass Learning Problems via Error-Correcting Output Codes - Dietterich, Bakiri - 1995
446	The Elements of Statistical Learning: Data Mining, Inference, and Prediction - Hastie, Tibshirani, et al. - 2001
435	Schapire R: A short introduction to boosting - Freund - 1999
383	An efficient boosting algorithm for combining preferences - Freund, Iyer, et al. - 1998
381	Estimating the support of a high-dimensional distribution - Schölkopf, Platt, et al. - 2001
373	BoosTexter: A boosting-based system for text categorization.” Machine learning 39.2 - Schapire, Singer - 2000
364	An experimental comparison of three methods for constructing ensembles of decision trees: bagging, boosting and randomization - Dietterich
358	Boosting a weak learning algorithm by majority - Freund - 1995
332	Reducing multiclass to binary: A unifying approach for margin classifiers - Allwein, Schapire, et al. - 2000
328	Decision theoretic generalization of the PAC model for neural net and other learning applications - Haussler - 1992
279	Cryptographic limitations on learning boolean formulae and finite automata - Kearns, Valiant - 1994
279	An introduction to kernel-based learning algorithms - Muller, Mika, et al. - 2001
265	Learning to order things - Cohen, Schapire, et al. - 1998
255	PL: Neural Network Learning: Theoretical Foundations - Anthony, Bartlett - 1999
242	The boosting approach to machine learning: an overview - Schapire - 2001
230	New support vector algorithms - Schölkopf, Smola, et al. - 2000
229	Regularization algorithms for learning that are equivalent to multilayer networks - Poggio, Girosi - 1990
221	Structural risk minimization over data-dependent hierarchies - Shawe-Taylor, Bartlett, et al. - 1940
210	Choosing multiple parameters for support vector machines - Chapelle, Vapnik, et al. - 2002