The Boosting Approach to Machine Learning An Overview

[1]  Michael Collins,et al.  Discriminative Reranking for Natural Language Parsing , 2000, CL.

[2]  David P. Helmbold,et al.  Boosting Methods for Regression , 2002, Machine Learning.

[3]  Ayhan Demiriz,et al.  Linear Programming Boosting via Column Generation , 2002, Machine Learning.

[4]  Gunnar Rätsch,et al.  Soft Margins for AdaBoost , 2001, Machine Learning.

[5]  Thomas G. Dietterich An Experimental Comparison of Three Methods for Constructing Ensembles of Decision Trees: Bagging, Boosting, and Randomization , 2000, Machine Learning.

[6]  Yoram Singer,et al.  Logistic Regression, AdaBoost and Bregman Distances , 2000, Machine Learning.

[7]  Yoram Singer,et al.  BoosTexter: A Boosting-based System for Text Categorization , 2000, Machine Learning.

[8]  Eric Bauer,et al.  An Empirical Comparison of Voting Classification Algorithms: Bagging, Boosting, and Variants , 1999, Machine Learning.

[9]  Corinna Cortes,et al.  Support-Vector Networks , 1995, Machine Learning.

[10]  R. Schapire The Strength of Weak Learnability , 1990, Machine Learning.

[11]  Srinivas Bangalore,et al.  Combining prior knowledge and boosting for call classification in spoken language dialogue , 2002, 2002 IEEE International Conference on Acoustics, Speech, and Signal Processing.

[12]  V. Koltchinskii,et al.  Empirical margin distributions and bounding the generalization error of combined classifiers , 2002, math/0405343.

[13]  J. Friedman Greedy function approximation: A gradient boosting machine. , 2001 .

[14]  Bhiksha Raj,et al.  A boosting approach for confidence scoring , 2001, INTERSPEECH.

[15]  Dmitry Panchenko,et al.  Further Explanation of the Effectiveness of Voting Methods: The Game between Margins and Weights , 2001, COLT/EuroCOLT.

[16]  Cesare Furlanello,et al.  Tuning Cost-Sensitive Boosting and Its Application to Melanoma Diagnosis , 2001, Multiple Classifier Systems.

[17]  Marilyn A. Walker,et al.  SPoT: A Trainable Sentence Planner , 2001, NAACL.

[18]  John D. Lafferty,et al.  Boosting and Maximum Likelihood for Exponential Models , 2001, NIPS.

[19]  Yoram Singer,et al.  Boosting for document routing , 2000, CIKM '00.

[20]  Lluís Màrquez i Villodre,et al.  Boosting Applied to Word Sense Disambiguation , 2000, ArXiv.

[21]  Yoram Singer,et al.  Reducing Multiclass to Binary: A Unifying Approach for Margin Classifiers , 2000, J. Mach. Learn. Res..

[22]  Gunnar Rätsch,et al.  Barrier Boosting , 2000, COLT.

[23]  Paul A. Viola,et al.  Boosting Image Retrieval , 2000, Proceedings IEEE Conference on Computer Vision and Pattern Recognition. CVPR 2000 (Cat. No.PR00662).

[24]  Eric Johnson,et al.  Predicting subscriber dissatisfaction and improving retention in the wireless telecommunications industry , 2000, IEEE Trans. Neural Networks Learn. Syst..

[25]  J. Friedman Special Invited Paper-Additive logistic regression: A statistical view of boosting , 2000 .

[26]  Peter L. Bartlett,et al.  Functional Gradient Techniques for Combining Hypotheses , 2000 .

[27]  Vladimir N. Vapnik,et al.  The Nature of Statistical Learning Theory , 2000, Statistics for Engineering and Information Science.

[28]  Dmitry Panchenko,et al.  Some New Bounds on the Generalization Error of Combined Classifiers , 2000, NIPS.

[29]  David P. Helmbold,et al.  Potential Boosters? , 1999, NIPS.

[30]  Peter L. Bartlett,et al.  Boosting Algorithms as Gradient Descent , 1999, NIPS.

[31]  Y. Freund,et al.  Adaptive game playing using multiplicative weights , 1999 .

[32]  Leo Breiman,et al.  Prediction Games and Arcing Algorithms , 1999, Neural Computation.

[33]  Yoram Singer,et al.  A simple, fast, and effective rule learner , 1999, AAAI 1999.

[34]  J. Lafferty Additive models, boosting, and inference for generalized divergences , 1999, COLT '99.

[35]  Robert E. Schapire,et al.  Drifting Games , 1999, COLT '99.

[36]  Manfred K. Warmuth,et al.  Boosting as entropy projection , 1999, COLT '99.

[37]  Yoav Freund,et al.  An Adaptive Version of the Boost by Majority Algorithm , 1999, COLT '99.

[38]  Yoav Freund,et al.  The Alternating Decision Tree Learning Algorithm , 1999, ICML.

[39]  Satoshi Shirai,et al.  Using Decision Trees to Construct a Practical Parser , 1999, COLING.

[40]  Thomas Richardson,et al.  Boosting methodology for regression problems , 1999, AISTATS.

[41]  Yoram Singer,et al.  Boosting Applied to Tagging and PP Attachment , 1999, EMNLP.

[42]  Peter L. Bartlett,et al.  Direct Optimization of Margins Improves Generalization in Combined Classifiers , 1998, NIPS.

[43]  Yoram Singer,et al.  Boosting and Rocchio applied to text filtering , 1998, SIGIR '98.

[44]  Yoram Singer,et al.  An Efficient Boosting Algorithm for Combining Preferences by , 2013 .

[45]  Yoram Singer,et al.  Improved Boosting Algorithms Using Confidence-rated Predictions , 1998, COLT' 98.

[46]  Dale Schuurmans,et al.  Boosting in the Limit: Maximizing the Margin of Learned Ensembles , 1998, AAAI/IAAI.

[47]  L. Breiman Arcing classifier (with discussion and a rejoinder by the author) , 1998 .

[48]  Peter L. Bartlett,et al.  The Sample Complexity of Pattern Classification with Neural Networks: The Size of the Weights is More Important than the Size of the Network , 1998, IEEE Trans. Inf. Theory.

[49]  Catherine Blake,et al.  UCI Repository of machine learning databases , 1998 .

[50]  L. Breiman Arcing Classifiers , 1998 .

[51]  Yoshua Bengio,et al.  Training Methods for Adaptive Boosting of Neural Networks , 1997, NIPS.

[52]  David W. Opitz,et al.  An Empirical Evaluation of Bagging and Boosting , 1997, AAAI/IAAI.

[53]  Robert E. Schapire,et al.  Using output codes to boost multiclass learning problems , 1997, ICML.

[54]  Yoav Freund,et al.  Boosting the margin: A new explanation for the effectiveness of voting methods , 1997, ICML.

[55]  Harris Drucker,et al.  Improving Regressors using Boosting Techniques , 1997, ICML.

[56]  John D. Lafferty,et al.  Inducing Features of Random Fields , 1995, IEEE Trans. Pattern Anal. Mach. Intell..

[57]  Joachim M. Buhmann,et al.  Pairwise Data Clustering by Deterministic Annealing , 1997, IEEE Trans. Pattern Anal. Mach. Intell..

[58]  J. Ross Quinlan,et al.  Bagging, Boosting, and C4.5 , 1996, AAAI/IAAI, Vol. 1.

[59]  Yoav Freund,et al.  Experiments with a New Boosting Algorithm , 1996, ICML.

[60]  Yoav Freund,et al.  Game theory, on-line prediction and boosting , 1996, COLT '96.

[61]  Christopher J. Merz,et al.  UCI Repository of Machine Learning Databases , 1996 .

[62]  Yoav Freund,et al.  A decision-theoretic generalization of on-line learning and an application to boosting , 1997, EuroCOLT.

[63]  Corinna Cortes,et al.  Boosting Decision Trees , 1995, NIPS.

[64]  Mark Craven,et al.  Learning Sparse Perceptrons , 1995, NIPS.

[65]  William W. Cohen Fast Effective Rule Induction , 1995, ICML.

[66]  金田 重郎,et al.  C4.5: Programs for Machine Learning (書評) , 1995 .

[67]  Thomas G. Dietterich,et al.  Solving Multiclass Learning Problems via Error-Correcting Output Codes , 1994, J. Artif. Intell. Res..

[68]  Vladimir Vapnik,et al.  The Nature of Statistical Learning , 1995 .

[69]  Johannes Fürnkranz,et al.  Incremental Reduced Error Pruning , 1994, ICML.

[70]  Leslie G. Valiant,et al.  Cryptographic limitations on learning Boolean formulae and finite automata , 1994, JACM.

[71]  Alberto Maria Segre,et al.  Programs for Machine Learning , 1994 .

[72]  Harris Drucker,et al.  Boosting Performance in Neural Networks , 1993, Int. J. Pattern Recognit. Artif. Intell..

[73]  J. Ross Quinlan,et al.  C4.5: Programs for Machine Learning , 1992 .

[74]  Bernhard E. Boser,et al.  A training algorithm for optimal margin classifiers , 1992, COLT '92.

[75]  Yoav Freund,et al.  Boosting a weak learning algorithm by majority , 1990, COLT '90.

[76]  David Haussler,et al.  Learnability and the Vapnik-Chervonenkis dimension , 1989, JACM.

[77]  David Haussler,et al.  What Size Net Gives Valid Generalization? , 1989, Neural Computation.

[78]  Leslie G. Valiant,et al.  A theory of the learnable , 1984, STOC '84.

[79]  J. Darroch,et al.  Generalized Iterative Scaling for Log-Linear Models , 1972 .

[80]  Vladimir Vapnik,et al.  Chervonenkis: On the uniform convergence of relative frequencies of events to their probabilities , 1971 .

[81]  Journal of the Association for Computing Machinery , 1961, Nature.