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Abstract
We introduce a new notion called Fourieraccessibility that allows us to precisely characterize the class of Boolean functions for which a standard greedy learning algorithm successfully learns all relevant attributes. If the target function is Fourieraccessible, then the success probability of the greedy algorithm can be made arbitrarily close to one. On the other hand, if the target function is not Fourieraccessible, then the error probability tends to one. Finally, we extend these results to the situation where the input data are corrupted by random attribute and classification noise and prove that greedy learning is quite robust against such errors.
Original language  English 

Title of host publication  COCOON 2007: Computing and Combinatorics 
Number of pages  11 
Volume  4598 LNCS 
Publisher  Springer Berlin Heidelberg 
Publication date  01.12.2007 
Pages  296306 
ISBN (Print)  9783540735441 
ISBN (Electronic)  9783540735458 
DOIs  
Publication status  Published  01.12.2007 
Event  13th Annual International Computing and Combinatorics Conference  Banff, Canada Duration: 16.07.2007 → 19.07.2007 Conference number: 70851 
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Dive into the research topics of 'When Does Greedy Learning of Relevant Attributes Succeed? A FourierBased Characterization'. Together they form a unique fingerprint.Projects
 1 Finished

Robust learning methods and data compression
01.01.04 → 31.12.08
Project: DFG Projects › DFG Individual Projects