electrolyte
发表于 2025-3-30 10:29:26
Stability and Generalization of Bipartite Ranking Algorithmsn bounds for ranking, which are based on uniform convergence and in many cases cannot be applied to these algorithms. A comparison of the bounds we obtain with corresponding bounds for classification algorithms yields some interesting insights into the difference in generalization behaviour between ranking and classification.
青春期
发表于 2025-3-30 13:16:52
http://reply.papertrans.cn/59/5829/582822/582822_52.png
pancreas
发表于 2025-3-30 17:11:30
Conference proceedings 2005ning Theory) held in Bertinoro, Italy from June 27 to 30, 2005. The technical program contained 45 papers selected from 120 submissions, 3 open problems selected from among 5 contributed, and 2 invited lectures. The invited lectures were given by Sergiu Hart on “Uncoupled Dynamics and Nash Equilibri
TIA742
发表于 2025-3-30 23:32:20
A New Perspective on an Old Perceptron Algorithmlgorithm in the inseparable case. We describe a multiclass extension of the algorithm. This extension is used in an experimental evaluation in which we compare the proposed algorithm to the Perceptron algorithm.
内疚
发表于 2025-3-31 03:40:07
http://reply.papertrans.cn/59/5829/582822/582822_55.png
吹气
发表于 2025-3-31 08:06:23
Ranking and Scoring Using Empirical Risk Minimizationking algorithms based on boosting and support vector machines. Just like in binary classification, fast rates of convergence are achieved under certain noise assumption. General sufficient conditions are proposed in several special cases that guarantee fast rates of convergence.
indecipherable
发表于 2025-3-31 12:35:33
Loss Bounds for Online Category Rankingounds for the algorithms by using the properties of the dual solution while imposing additional constraints on the dual form. Finally, we outline and analyze the convergence of a general update that can be employed with any Bregman divergence.
endoscopy
发表于 2025-3-31 15:30:03
The Value of Agreement, a New Boosting Algorithmearners will result in a larger improvement whereas using two copies of a single algorithm gives no advantage at all. As a proof of concept, we apply the algorithm, named AgreementBoost, to a web classification problem where an up to 40% reduction in the number of labeled examples is obtained.