generalization bound的作用泛化边界(generalization bound)是一种评估机器学习模型泛化性能的方法,旨在使用训练数据集和网络参数来预测测试误差。尽管目前的泛化边界很少能对实际测试错误产生良好的预测,但它们在架构设计和训练算法等方面提供了有价值的见解。©2022 Baidu |由 百度智能云 提供计算服务 | 使用百度前必读 ...
The oversampling approach is often used for binary imbalanced classification. We demonstrate that the approach can be interpreted as the weighted classification and derived a generalization bound for it. The bound can be used for more accurate re-balancing of classes. Results of computational ...
元泛化误差界限(Meta-Generalization Error Bound)是元学习中用于衡量模型从训练任务中学习到的知识在新任务上的泛化能力的一个理论界限。它通常表示为模型在训练任务上的表现与在未见过的新任务上的表现之间的差异。 定义与解释 在元学习中,我们希望学习到的模型参数能够在见到新的任务时快速适应并取得良好表现。元泛...
3) Upper Bound of Generalization Error 推广性误差上界4) spread of the nature 性质的推广 例句>> 5) VC generalization bounds VC推广界 1. The novel algorithm for linear SVM Regression(SVR) is proposed based on VC generalization bounds and applied to comprehensive development of county. 提出...
generalization boundutility maximizationlearning theoryDrawing on statistical learning theory, we derive out-of-sample and optimality guarantees about the investment strategy obtained from a regularized portfolio optimization model which attempts to exploit side information about the financial market in order ...
有了KL-divergence的bound,作者分析了Langevin dynamics (LD) algorithm [4],并给出了更紧的界。 这里首先考虑一个general的迭代算法:初始值相同,经过T个iterations时KL散度的界,从而可以对上面的EGE进行约束。 这里W_{t|}代表给出W_0...W_{t-1},W_t的条件概率。 随后在LD 算法下得到泛化风险界。 实验...
Abstract Recently, metric learning and similarity learning have attracted a large amount of interest. Many models and optimization algorithms have been proposed. However, there is relatively little work on the generalization analysis of such methods. In this paper, we derive novel generalization bounds...
4.(Logic)logicthe derivation of a general statement from a particular one, formally by prefixing a quantifier and replacing a subject term by a bound variable. If the quantifier is universal (universal generalization) the argument is not in general valid; if it is existential (existential genera...
First,weshowhowvariousnotionsofstabilityupper-andlower-boundthebias andvarianceofseveralestimatorsoftheexpectedperformanceforgenerallearning algorithms.Aweakstabilityconditionisshowntobeequivalenttoconsistencyof empiricalriskminimization. Thesecondpartofthethesisderivestightperformanceguaranteesforgreedy ...
A bound on the error of cross validation using the approximation and estimation rates, with consequences for the training-test split. In Proceedings of the 8th International Conference on Neural Information Processing Systems, NIPS’95, 183–189 (MIT Press, 1995). Li, Y. et al. Individual-...