Z, around which accurate nonasymptotic confidence bounds can be built, even when Z does not exhibit a sub-Gaussian tail behavior. Thanks to the high confidence it achieves on heavy-tailed data, MoM has found various applications in machine learning, where it is used to design training ...
Generalization Bounds 来自 Springer 喜欢 0 阅读量: 333 作者: M Reid 摘要: Inequalities; Sample complexity In the theory of statistical machine learning, a generalization bound – or, more precisely, a generalization error bound – is a statement about the predictive... 关键词: Quantitative ...
Generalization Error Bounds for Noisy, Iterative Algorithms In statistical learning theory, generalization error is used to quantify the degree to which a supervised machine learning algorithm may overfit to trainin... A Pensia,V Jog,PL Loh - IEEE 被引量: 7发表: 2018年 Characterizing Membership ...
Generalization Bounds Mark Reid Get Access This is an excerpt from the content Synonyms Inequalities; Sample complexity Definition In the theory of statistical machine learning, a generalization bound – or, more precisely, a generalization error bound – is a statement about the predictive perform...
We derive generalization bounds for learning algorithms based on their robustness: the property that if a testing sample is "similar" to a training sample, then the testing error is close to the training error. This provides a novel approach, different from complexity or stability arguments, to ...
In our experiments, we compute generalization bounds for random forests on various benchmark data sets. Because the individual decision trees already perform ... SS Lorenzen,C Igel,Y Seldin - 《Machine Learning》 被引量: 0发表: 2019年 Property Testing Lower Bounds via a Generalization of Randomi...
Obtaining generalization bounds for learning algorithms is one of the main subjects studied in theoretical machine learning. In recent years, information-theoretic bounds on generalization have gained the attention of researchers. This a... H Hafez-Kolahi,Z Golgooni,S Kasaei,... 被引量: 0发表:...
In response, our paper unveils an information-theoretic generalization framework for FL. Specifically, it quantifies generalization errors by evaluating the information entropy of local distributions and discerning discrepancies across these distributions. Inspired by our deduced generalization bounds, we ...
GENERALIZATION BOUNDS FOR DOMAIN ADAPTATION VIA DOMAIN TRANSFORMATIONS The following topics are dealt with: learning (artificial intelligence); Bayes methods; Gaussian processes; neural nets; convolution; feedforward neural ne... E Vural - IEEE International Workshop on Machine Learning for Signal ...
Achieving small prediction errorR(α) is the ultimate goal of (quantum) machine learning. AsPis generally not known, the training error{\hat{R}}_{S}({{{\boldsymbol{\alpha }}})is often taken as a proxy forR(α). This strategy can be justified via bounds on thegeneralization error...