LogitBoost
In machine learning and computational learning theory, LogitBoost is a boosting algorithm formulated by Jerome Friedman, Trevor Hastie, and Robert Tibshirani. The original paper casts the AdaBoost algorithm into a statistical framework.[1] Specifically, if one considers AdaBoost as a generalized additive model and then applies the cost function of logistic regression, one can derive the LogitBoost algorithm.
Minimizing the LogitBoost cost function
LogitBoost can be seen as a convex optimization. Specifically, given that we seek an additive model of the form
the LogitBoost algorithm minimizes the logistic loss:
gollark: It probably isn't meaningfully.
gollark: It's not actually a security threat whatsoever to just have virus code lying around, if there isn't anything to actually run it.
gollark: I believe browsers run media decoding heavily sandboxed nowadays, not that this is foolproof because ææææææææææa all computer systems are horribly broken.
gollark: But a virus and something which is detected as a virus are different.
gollark: You probably can't outside of the omnipresent media stack bugs.
See also
References
- Friedman, Jerome; Hastie, Trevor; Tibshirani, Robert (2000). "Additive logistic regression: a statistical view of boosting". Annals of Statistics. 28 (2): 337–407. CiteSeerX 10.1.1.51.9525. doi:10.1214/aos/1016218223.
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