Titlebook: Learning Theory; 18th Annual Conferen Peter Auer,Ron Meir Conference proceedings 2005 Springer-Verlag Berlin Heidelberg 2005 Boosting.Suppo

斑駁

Stability and Generalization of Bipartite Ranking Algorithmscently gained attention in machine learning. We study generalization properties of ranking algorithms, in a particular setting of the ranking problem known as the bipartite ranking problem, using the notion of algorithmic stability.In particular, we derive generalization bounds for bipartite ranking

CRUMB

Loss Bounds for Online Category Rankinggorithms for . category ranking where the instances are revealed in a sequential manner. We describe additive and multiplicative updates which constitute the core of the learning algorithms. The updates are derived by casting a constrained optimization problem for each new instance. We derive loss b

無表情

Margin-Based Ranking Meets Boosting in the Middlee. Our bound suggests that algorithms that maximize the ranking margin generalize well..We then describe a new algorithm, Smooth Margin Ranking, that precisely converges to a maximum ranking-margin solution. The algorithm is a modification of RankBoost, analogous to Approximate Coordinate Ascent Boo

IDEAS

The Value of Agreement, a New Boosting Algorithmfier’s quality and thus reduce the number of labeled examples necessary for achieving it. This is achieved by demanding from the algorithms generating the classifiers to agree on the unlabeled examples. The extent of this improvement depends on the diversity of the learners—a more diverse group of l

Instantaneous

conscience

Generalization Error Bounds Using Unlabeled Dataement probability of pairs of classifiers using unlabeled data. The first method works in the realizable case. It suggests how the ERM principle can be refined using unlabeled data and has provable optimality guarantees when the number of unlabeled examples is large. Furthermore, the technique exten

薄膜

On the Consistency of Multiclass Classification Methodsproperty of Bayes consistency. We provide a necessary and sufficient condition for consistency which applies to a large class of multiclass classification methods. The approach is illustrated by applying it to some multiclass methods proposed in the literature.

邊緣

Analogy

synovium

Tracking the Best of Many Experts provided that the set of experts has a certain structure allowing efficient implementations of the exponentially weighted average predictor. As an example we work out the case where each expert is represented by a path in a directed graph and the loss of each expert is the sum of the weights over t