標(biāo)題: Titlebook: Empirical Inference; Festschrift in Honor Bernhard Sch?lkopf,Zhiyuan Luo,Vladimir Vovk Book 2013 Springer-Verlag Berlin Heidelberg 2013 Bay [打印本頁] 作者: expenditure 時間: 2025-3-21 19:28
書目名稱Empirical Inference影響因子(影響力)
書目名稱Empirical Inference影響因子(影響力)學(xué)科排名
書目名稱Empirical Inference網(wǎng)絡(luò)公開度
書目名稱Empirical Inference網(wǎng)絡(luò)公開度學(xué)科排名
書目名稱Empirical Inference被引頻次
書目名稱Empirical Inference被引頻次學(xué)科排名
書目名稱Empirical Inference年度引用
書目名稱Empirical Inference年度引用學(xué)科排名
書目名稱Empirical Inference讀者反饋
書目名稱Empirical Inference讀者反饋學(xué)科排名
作者: Substance-Abuse 時間: 2025-3-21 23:26
Sonnenbad, Schlaf und Rhythmus,te of Control Sciences of the Russian Academy of Sciences, Moscow, Russia) in the framework of the “Generalised Portrait Method” for computer learning and pattern recognition. The development of these ideas started in 1962 and they were first published in 1964.作者: 嚴(yán)厲批評 時間: 2025-3-22 00:58 作者: DUST 時間: 2025-3-22 05:29
https://doi.org/10.1007/978-3-662-58719-5k and inaccurate rules. The AdaBoost algorithm of Freund and Schapire was the first practical boosting algorithm, and remains one of the most widely used and studied, with applications in numerous fields. This chapter aims to review some of the many perspectives and analyses of AdaBoost that have be作者: 變色龍 時間: 2025-3-22 10:52 作者: 陶瓷 時間: 2025-3-22 13:34
Birgit Piechulla,Hans Walter Heldting and in the general learningGeneral learning setting introduced by Vladimir Vapnik. We survey classic results characterizing learnability in terms of suitable notions of complexity, as well as more recent results that establish the connection between learnability and stability of a learning algor作者: 陶瓷 時間: 2025-3-22 19:02 作者: Foreknowledge 時間: 2025-3-22 22:42
A. Ullrich,B. Münzenberger,R. F. Hüttl We review some of the most well-known methods and discuss their advantages and disadvantages. Particular emphasis is put on methods that scale well at training and testing time so that they can be used in real-life systems; we discuss their application on large-scale image and text classification t作者: 使成核 時間: 2025-3-23 01:56
Friedemann Klenke,Markus Schollere method is identical to a formula in Bayesian statistics, but Kernel Ridge Regression has performance guarantees that have nothing to do with Bayesian assumptions. I will discuss two kinds of such performance guarantees: those not requiring any assumptions whatsoever, and those depending on the ass作者: 突襲 時間: 2025-3-23 08:56
Das Blatt als photosynthetisches System,. We discuss the foundations as well as some of the recent advances of the field, including strategies for learning or refining the measure of task relatedness. We present an example from the application domain of Computational Biology, where multi-task learning has been successfully applied, and gi作者: 一再遛 時間: 2025-3-23 10:47
Die Aufnahme der Aschenelementea given problem, and rule out others. We formulate the hypothesis that semi-supervised learning can help in an anti-causal setting, but not in a causal setting, and corroborate it with empirical results.作者: MIRE 時間: 2025-3-23 15:32
Die Zelle als metabolisches System,d simple estimators of the minimum mean squared error Mean squared error—(Minimum mean squared error—(., and prove their strong consistenciesConsistency—(. We bound the rate of convergenceRate of convergence, too.作者: 打谷工具 時間: 2025-3-23 21:48 作者: 情感 時間: 2025-3-23 23:34
Gesellschaftsentwicklung (Syndynamik),thm based on a completely different approach, tailored for transductive settingsTransductive setting—(Transductive online learning—(, which combines “random playout” and randomized rounding of loss subgradients. As an application of our approach, we present the first computationally efficient online作者: EWER 時間: 2025-3-24 04:07 作者: GUILT 時間: 2025-3-24 07:53
Die Wagen- und Schlittenkenntni?This short contribution presents the first paper in which Vapnik and Chervonenkis describe the foundations of Statistical Learning Theory (Vapnik, Chervonenkis (1968) Proc USSR Acad Sci 181(4): 781–783).作者: Retrieval 時間: 2025-3-24 14:36 作者: deceive 時間: 2025-3-24 17:58 作者: 種植,培養(yǎng) 時間: 2025-3-24 22:52 作者: Hearten 時間: 2025-3-25 01:17
On the Uniform Convergence of the Frequencies of Occurrence of Events to Their ProbabilitiesThis chapter is a translation of Vapnik and Chervonenkis’s pathbreaking note.essentially following the excellent translation.by Lisa Rosenblatt (the editors only corrected a few minor mistakes and in some places made the translation follow more closely the Russian original)..(Presented by Academician V. A. Trapeznikov, 6 October 1967)作者: fertilizer 時間: 2025-3-25 06:30
PAC-Bayesian TheoryThe PAC-Bayesian framework is a frequentist approach to machine learning which encodes learner bias as a “prior probability” over hypotheses. This chapter reviews basic PAC-Bayesian theory, including Catoni’s basic inequality and Catoni’s localization theorem.作者: entitle 時間: 2025-3-25 09:43
978-3-662-52511-1Springer-Verlag Berlin Heidelberg 2013作者: transplantation 時間: 2025-3-25 15:01
Bernhard Sch?lkopf,Zhiyuan Luo,Vladimir VovkHonours one of the pioneers of machine learning.Contributing authors are among the leading authorities in these domains.Of interest to researchers and engineers in the fields of machine learning, stat作者: 闡明 時間: 2025-3-25 17:23
http://image.papertrans.cn/e/image/308861.jpg作者: 石墨 時間: 2025-3-25 23:02
Sonnenbad, Schlaf und Rhythmus,te of Control Sciences of the Russian Academy of Sciences, Moscow, Russia) in the framework of the “Generalised Portrait Method” for computer learning and pattern recognition. The development of these ideas started in 1962 and they were first published in 1964.作者: esoteric 時間: 2025-3-26 03:21 作者: 現(xiàn)實 時間: 2025-3-26 08:21 作者: GRAIN 時間: 2025-3-26 11:43 作者: 處理 時間: 2025-3-26 16:30
Die Zelle als metabolisches System,d simple estimators of the minimum mean squared error Mean squared error—(Minimum mean squared error—(., and prove their strong consistenciesConsistency—(. We bound the rate of convergenceRate of convergence, too.作者: consolidate 時間: 2025-3-26 20:41
Early History of Support Vector Machineste of Control Sciences of the Russian Academy of Sciences, Moscow, Russia) in the framework of the “Generalised Portrait Method” for computer learning and pattern recognition. The development of these ideas started in 1962 and they were first published in 1964.作者: 詢問 時間: 2025-3-26 22:17
On Learnability, Complexity and Stabilitying and in the general learningGeneral learning setting introduced by Vladimir Vapnik. We survey classic results characterizing learnability in terms of suitable notions of complexity, as well as more recent results that establish the connection between learnability and stability of a learning algorithm.作者: 中止 時間: 2025-3-27 03:55
Kernel Ridge Regressione method is identical to a formula in Bayesian statistics, but Kernel Ridge Regression has performance guarantees that have nothing to do with Bayesian assumptions. I will discuss two kinds of such performance guarantees: those not requiring any assumptions whatsoever, and those depending on the assumption of randomness.作者: 上漲 時間: 2025-3-27 06:55
Semi-supervised Learning in Causal and Anticausal Settingsa given problem, and rule out others. We formulate the hypothesis that semi-supervised learning can help in an anti-causal setting, but not in a causal setting, and corroborate it with empirical results.作者: 謊言 時間: 2025-3-27 11:18
Strong Universal Consistent Estimate of the Minimum Mean Squared Errord simple estimators of the minimum mean squared error Mean squared error—(Minimum mean squared error—(., and prove their strong consistenciesConsistency—(. We bound the rate of convergenceRate of convergence, too.作者: FLASK 時間: 2025-3-27 13:48 作者: 鳴叫 時間: 2025-3-27 18:47
Modellrechnungen im Grundwasser,onger generalization bounds. Therefore, we propose algorithms to find the deepest hypothesis. Following the definitions of depth in multivariate statistics, we refer to the deepest hypothesis as the median hypothesis. We show that similarly to the univariate and multivariate medians, the median hypo作者: 可商量 時間: 2025-3-27 22:07 作者: intoxicate 時間: 2025-3-28 04:33
The Median Hypothesisonger generalization bounds. Therefore, we propose algorithms to find the deepest hypothesis. Following the definitions of depth in multivariate statistics, we refer to the deepest hypothesis as the median hypothesis. We show that similarly to the univariate and multivariate medians, the median hypo作者: ODIUM 時間: 2025-3-28 08:54 作者: beta-cells 時間: 2025-3-28 10:56 作者: harbinger 時間: 2025-3-28 17:48
Explaining AdaBoostk and inaccurate rules. The AdaBoost algorithm of Freund and Schapire was the first practical boosting algorithm, and remains one of the most widely used and studied, with applications in numerous fields. This chapter aims to review some of the many perspectives and analyses of AdaBoost that have be作者: peptic-ulcer 時間: 2025-3-28 21:17 作者: 大暴雨 時間: 2025-3-29 02:03
On Learnability, Complexity and Stabilitying and in the general learningGeneral learning setting introduced by Vladimir Vapnik. We survey classic results characterizing learnability in terms of suitable notions of complexity, as well as more recent results that establish the connection between learnability and stability of a learning algor作者: FLIRT 時間: 2025-3-29 06:03
Loss Functionsf the loss functionsLoss function—( used to evaluate performance (0-1 lossLoss@0-1 Loss, squared lossSquared loss, and log lossLog loss, respectively). But there are many other loss functions one could use. In this chapter I will summarise some recent work by me and colleagues studying the theoretic作者: AER 時間: 2025-3-29 08:08
Statistical Learning Theory in Practice We review some of the most well-known methods and discuss their advantages and disadvantages. Particular emphasis is put on methods that scale well at training and testing time so that they can be used in real-life systems; we discuss their application on large-scale image and text classification t作者: 表否定 時間: 2025-3-29 14:27 作者: 卷發(fā) 時間: 2025-3-29 17:28 作者: –scent 時間: 2025-3-29 23:26
Semi-supervised Learning in Causal and Anticausal Settingsa given problem, and rule out others. We formulate the hypothesis that semi-supervised learning can help in an anti-causal setting, but not in a causal setting, and corroborate it with empirical results.作者: kidney 時間: 2025-3-30 00:49
Strong Universal Consistent Estimate of the Minimum Mean Squared Errord simple estimators of the minimum mean squared error Mean squared error—(Minimum mean squared error—(., and prove their strong consistenciesConsistency—(. We bound the rate of convergenceRate of convergence, too.作者: Chronic 時間: 2025-3-30 04:42
The Median Hypothesis question: what is the best hypothesis to select from a given hypothesis class? To address this question we adopt a PAC-Bayesian approach. According to this viewpoint, the observations and prior knowledge are combined to form a belief probability over the hypothesis class. Therefore, we focus on the作者: 啞劇 時間: 2025-3-30 08:51 作者: GLUE 時間: 2025-3-30 14:42
Pivotal Estimation in High-Dimensional Regression via Linear Programmingroscedasticity, and does not require knowledge of the variance of random errors. The method is based on linear programming only, so that its numerical implementation is faster than for previously known techniques using conic programs, and it allows one to deal with higher-dimensional models. We prov作者: machination 時間: 2025-3-30 19:07 作者: Keshan-disease 時間: 2025-3-30 22:16
https://doi.org/10.1007/978-3-662-36877-0uation of fixed design and i.i.d. Gaussian errors with known variance. Following Gautier and Tsybakov (High-dimensional instrumental variables regression and confidence sets. ArXiv e-prints 1105.2454, 2011), we obtain the results under weaker sensitivity assumptions than the restricted eigenvalue or assimilated conditions.作者: Cloudburst 時間: 2025-3-31 03:32 作者: liposuction 時間: 2025-3-31 05:24 作者: 蛙鳴聲 時間: 2025-3-31 10:09
Some Remarks on the Statistical Analysis of SVMs and Related Methodscade witnessed a shift towards consistencyConsistency, oracle inequalities, and learning ratesLearning rate. We discuss some of these developments in view of binary classificationBinary classification and least squares regressionLeast squares regression.作者: 享樂主義者 時間: 2025-3-31 14:37
Explaining AdaBoostsed and studied, with applications in numerous fields. This chapter aims to review some of the many perspectives and analyses of AdaBoost that have been applied to explain or understand it as a learning method, with comparisons of both the strengths and weaknesses of the various approaches.
