Advantages of adaboost over gentleboost in applications, or vice versa?

I've been researching on AdaBoost and GentleBoost classifiers, but can't seem to find a clear answer to the question:

• What is Adaboost better at classifying in computer vision?

• What is GentleBoost better at classifying?

I've been told that AdaBoost is good for things with soft edges, like facial recognition, while GentleBoost is good for things with harder and more symmetrical features and edges, like vehicles. Is this true? Is there any proof for this or any evidence to back up this claim?

Genetellboost is developed to cope with labeling noise, i.e., when some samples are wrongly labeled. Though, it wasn't so successful in that respect, from my experience it is quite good in handling noisy features which are kind of outliers in dataset. That being said, there is no proof for neither of the things that you asked (nor the things that you mentioned are true). In fact, we know that these algorithms are so similar that it is practically impossible to predict which one works better on which dataset. You have to test it and that's it.

StackExchange Computer Science Q#23316, answer score: 4