该分布是在一个可测空间\[\mathcal{Z}\]上的一个概率测度。
In particular, the fast-rate bound is equivalent to the Seeger--Langford bound. Secondly, for losses with more general tail behaviors, we introduce two new parameter-free bounds: a PAC-Bayes Chernoff analogue when the loss' cumulative generating function is bounded, and a bound when the loss'...
PAC-Bayes boundStatistical learning theorySupport vector machineMulti-view learningMulti-view learning is a widely applicable research direction. This paper presents eight PAC-Bayes bounds to analyze the generalization performance of multi-view classifiers. These bounds adopt data dependent Gaussian priors ...
基于 PAC-Bayes 边界理论存在的优势, 本文将 PAC- Bayes边界理论应用于 SVM模型选择问题, 将 PAC-Bayes 边界 ( PAC-Bayes Bound, PBB ) 与基于交叉验证的网格搜 索法 ( Grid Search, GS ) 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 SVM 惩罚 系数和核函数参数的快速...
This paper proposes a PAC-Bayes bound to measure the performance of Support Vector Machine (SVM) classifiers. The bound is based on learning a prior over the distribution of classifiers with a part of the training samples. Experimental work shows that this bound is tighter than the original PAC...
Our bounds are very general, since being able to find an upper bound on the fractional chromatic number of the dependency graph is sufficient to get new Pac-Bayes bounds for specific settings. We show how our results can be used to derive bounds for ranking statistics (such as Auc) and ...
PACBayesBound,SupportVectorMachines,Generalization prediction,ModelSelection 1Introduction Supportvectormachines(SVM)[3]areacceptedamongpractitionersasone ofthemostaccurateautomaticclassificationtechniques.Theyimplement linearclassifiersinahigh-dimensionalfeaturespaceusingthekerneltrick toenableadualrepresentationandeffi...
(SVM).Then,this paper discusses PAC—Bayes bound of ma ny machine learning algor ithms,a n d specially a nalyzes t he bou n d、) lrim the non—IID data.Furthermore,this paper elaborates research status and development of the PAC—Bayes bound application f rom four directions,and ...
如何理解PAC Bayesian的bound? 需要注意的是,由于数据的真实分布是没办法知道的,期望风险 一般是很难通过计算得到,而经验误差是一个对于它的无偏的代替,PAC-Bayes框架可以给出关于期望风险和经验风险的不等式。为了给… 阅读全文 赞同 98 条评论 ...
[6] TAIJI SUZUKI.PAC-Bayesian Bound for Gaussian Process Regression and Multiple Kernel Additive Model[J].25th Annual Conference on Learning Theory,Workshop and Conference Proceedings,2012,23(8):1-20. [7] MAHDI MILANNI FARD,JOELLE PINEAU.PAC-Bayesian Model Selection forReinforcement Learning[M]...