Yoav Freund - A Decision-Theoretic Generalization of On-Line Learning and an Application to Boosting (1995)

Context

Learned in this study

Things to explore

Overview

Notes

1. Introduction

Boosting

• Suppose that we wish to create a computer program that will accurately predict the winner of a horse race based on the usual information (number of races recently won by each horse, betting odds for each horse, etc.)
• To create such a program, we ask our favorite expert to explain his betting strategy. Not surprisingly, the expert is unable to articulate a grand set of rules for selecting a horse
• When presented with the data for a specific set of races, the expert has no trouble coming up with a "rule-of-thumb" for that set of races
• By repeatedly asking the expert's opinion on different collections of races, we are able to extract many rules-of-thumb
• In order to use these rules-of-thumb to maximum advantage, there are two problems we face:
  • How should we choose the collections of races presented to the expert so as to extract rules-of-thumb from the expert that will be the most useful
  • Once we have collected many rules-of-thumb, how can they be combined into a single, highly accurate predictor

See also

References

• Freund, Yoav, and Robert E. Schapire. "A desicion-theoretic generalization of on-line learning and an application to boosting." European conference on computational learning theory. Springer, Berlin, Heidelberg, 1995.