HE Li.Survey on PAC-Bayes theory and applicat ion research.Journal of Frontiers of Computer Science and Technology,2015,9(1):1-13. Abstract:PAC—Bayes theor y integrating theories of Bayesian paradigm a n d structure risk minimization for stochastic classif iers has been considered as a fram ...
PAC-Bayes 是一个通用框架，可以有效地审视众多机器学习算法的泛化能力。该框架利用贝叶斯学习的灵活性，...
需要金币:*** 金币(10金币=人民币1元) PAC-Bayes理论及应用研究综述.pdf 关闭预览 想预览更多内容,点击免费在线预览全文 免费在线预览全文 汤莉,宫秀军,何丽.PAC-Bayes理论及应用研究综述[J].计算机科学与探索,2015 ,9(1):1-13. ISSN 1673-9418 CODEN JKYTA8 E-mail: fcst@vip. 163.com Journal of Fr...
PAC_Byes理论系列1:(初识)如何理解 0. 该专栏写作初衷:(因为我发现网上关于PAC-bayes理论的介绍很少,相关资料大多都是中英文论文,所以开这个专栏的初衷,是利用分享的形式,加深自己对此理论的理解,同时进一步向大家简单介绍,想要更… 阅读全文 如何理解PAC Bayesian的bound?
支持向量机算法PAC-Bayes边界 理论与实验研究天津大学汤莉天津大学
针对支持向量机(SVM)模型选择问题,通过分析PAC-Bayes边界理论框架及其在SVM上的应用,将PAC-Bayes边界理论与基于交叉验证的网格搜索法相结合,提出一种基于PAC-Bayes边界的SVM模型选择方法(PBB-GS),实现快速优选SVM的惩罚系数和核函数参数。UCI数据集的实验结果表明该方法优选出的参数能使SVM具有较高的泛化性能,并具有...
We explore the family of methods "PAC-Bayes with Backprop" (PBB) to train probabilistic neural networks by minimizing PAC-Bayes bounds. We present two training objectives, one derived from a previously known PAC-Bayes bound, and a second one derived from a novel PAC-Bayes bound. Both ...
We also obtain new bounds for data-dependent priors and unbounded loss functions. Optimizing these bounds naturally gives rise to a method called Information Complexity Minimization for which we discuss two practical examples for learning with neural networks, namely Entropy- and PAC-Bayes- SGD. ...
通过PAC-Bayes bounds方法解释了零样本学习提示工程的成功现象,并得到了相对紧密的泛化界。【转发】@爱可可-爱生活:[LG]《Understanding prompt engineering may not require rethinking generalization》V Aki...
bounds for practical applications appear to be the PAC-Bayes bound [5] and in particular the one given in [1], with a data dependent prior. Another issue affected by the ability to predict the generalisation capabil- ity of a classifier is the selection of the hyperparameters that ...