Immobilize 发表于 2025-3-30 09:50:51
http://reply.papertrans.cn/24/2326/232577/232577_51.png辩论的终结 发表于 2025-3-30 15:57:14
http://reply.papertrans.cn/24/2326/232577/232577_52.png拒绝 发表于 2025-3-30 18:46:47
https://doi.org/10.1007/978-3-658-00686-0their images under primitive recursive operators. The following is shown: This notion of learnability does not change if the class of primitive recursive operators is replaced by a larger enumerable class of operators. A class is hyperrobustly Ex-learnable iff it is a subclass of a recursively enumeVldl379 发表于 2025-3-30 21:05:56
https://doi.org/10.1007/978-3-658-00686-0ind change complexity bounds for learnability of these classes both from positive facts and from positive and negative facts..Building on Angluin’s notion of finite thickness and Wright’s work on finite elasticity, Shinohara defined the property of bounded finite thickness to give a sufficient condi慎重 发表于 2025-3-31 01:40:20
,, — Anlagen zur Montage flächiger Bauteile,strictions. This allows the use of tools such as regularization from the theory of (supervised) risk minimization for unsupervised settings. Moreover, this setting is very closely related to both principal curves and the generative topographic map..We explore this connection in two ways: 1) we propo苦笑 发表于 2025-3-31 07:25:12
http://reply.papertrans.cn/24/2326/232577/232577_56.png不能约 发表于 2025-3-31 09:15:55
https://doi.org/10.1007/978-3-642-96455-8ch are arbitrarily close to Yang’s minimax lower bounds, if the a posteriori probability function is in the classes used by Stone and others. The rates equal to the ones on the corresponding regression estimation problem. Thus for these classes classification is not easier than regression estimation金丝雀 发表于 2025-3-31 15:34:44
http://reply.papertrans.cn/24/2326/232577/232577_58.pnggangrene 发表于 2025-3-31 19:53:46
0302-9743 Overview: Includes supplementary material: 978-3-540-65701-9978-3-540-49097-5Series ISSN 0302-9743 Series E-ISSN 1611-3349倾听 发表于 2025-4-1 00:54:30
Modifikation der Modellstrukturey theoretical work on boosting including analyses of AdaBoost’s training error and generalization error, connections between boosting and game theory, methods of estimating probabilities using boosting, and extensions of AdaBoost for multiclass classification problems. We also briefly mention some empirical work.