The following work is a preprint collection of formal proofs regarding the convergence properties of the AdaBoost machine learning algorithm's classifier and margins. Various math and computer science papers have been written regarding conjectures and special cases of these convergence properties. Furthermore, the margins of AdaBoost feature prominently in the research surrounding the algorithm. At the zenith of this paper we present how AdaBoost's classifier and margins converge on a value that agrees with decades of research. After this, we show how various quantities associated with the combined classifier converge
Much attention has been paid to the theoretical explanation of the empirical success of AdaBoost. Th...
LPBoost seemingly should have better generalization ca- pability than AdaBoost according to the marg...
In order to understand AdaBoost’s dynamics, especially its ability to maximize margins, we derive an...
AdaBoost produces a linear combination of base hypotheses and predicts with the sign of this linear ...
AdaBoost produces a linear combination of base hypotheses and predicts with the sign of this linear ...
The “minimum margin ” of an ensemble classifier on a given training set is, roughly speaking, the sm...
Much attention has been paid to the theoretical explanation of the empirical success of AdaBoost. Th...
Editor: Much attention has been paid to the theoretical explanation of the empirical success of AdaB...
The iterative weight update for the AdaBoost machine learning algorithm may be realized as a dynamic...
Much attention has been paid to the theoretical explanation of the empirical success of AdaBoost. Th...
This thesis is in the field of machine learning: the use of data to automatically learn a hypothesis...
The risk, or probability of error, of the classifier produced by the AdaBoost algorithm is investiga...
Much attention has been paid to the theo-retical explanation of the empirical success of AdaBoost. T...
We have recently proposed an extension of ADABOOST to regression that uses the median of the base re...
0 0 1 Cumulative training margin distributions for AdaBoost versus our "Direct Optimization Of...
Much attention has been paid to the theoretical explanation of the empirical success of AdaBoost. Th...
LPBoost seemingly should have better generalization ca- pability than AdaBoost according to the marg...
In order to understand AdaBoost’s dynamics, especially its ability to maximize margins, we derive an...
AdaBoost produces a linear combination of base hypotheses and predicts with the sign of this linear ...
AdaBoost produces a linear combination of base hypotheses and predicts with the sign of this linear ...
The “minimum margin ” of an ensemble classifier on a given training set is, roughly speaking, the sm...
Much attention has been paid to the theoretical explanation of the empirical success of AdaBoost. Th...
Editor: Much attention has been paid to the theoretical explanation of the empirical success of AdaB...
The iterative weight update for the AdaBoost machine learning algorithm may be realized as a dynamic...
Much attention has been paid to the theoretical explanation of the empirical success of AdaBoost. Th...
This thesis is in the field of machine learning: the use of data to automatically learn a hypothesis...
The risk, or probability of error, of the classifier produced by the AdaBoost algorithm is investiga...
Much attention has been paid to the theo-retical explanation of the empirical success of AdaBoost. T...
We have recently proposed an extension of ADABOOST to regression that uses the median of the base re...
0 0 1 Cumulative training margin distributions for AdaBoost versus our "Direct Optimization Of...
Much attention has been paid to the theoretical explanation of the empirical success of AdaBoost. Th...
LPBoost seemingly should have better generalization ca- pability than AdaBoost according to the marg...
In order to understand AdaBoost’s dynamics, especially its ability to maximize margins, we derive an...
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