检索结果-内蒙古大学图书馆

Weak aggregating algorithm for the distribution-free perishable inventory problem

OPERATIONS RESEARCH LETTERS 2010年第6期38卷 516-521页

作者： Levina, Tatsiana Levin, Yuri McGill, Jeff Nediak, Mikhail Vovk, Vladimir Queens Univ Sch Business Kingston ON K7L 3N6 Canada Univ London Dept Comp Sci Egham TW20 0EX Surrey England

We formulate the multiperiod, distribution-free perishable inventory problem as a problem of prediction with expert advice and apply an online learning method (the Weak aggregating algorithm) to solve it. We show that the asymptotic average performance of this method is as good as that of any time-dependent stocking rule in a given parametric class. (C) 2010 Elsevier B.V. All rights reserved.

关键词： Online learning Newsvendor problem aggregating algorithm

来源：评论

学校读者我要写书评

暂无评论

Iterative ridge regression using the aggregating algorithm *

引用

PATTERN RECOGNITION LETTERS 2022年 158卷 34-41页

作者： Jamil, Waqas Bouchachia, Abdelhamid Bournemouth Univ Dept Comp & Informat Poole BH12 5BB England

In this paper, regularised regression for sequential data is investigated and a new ridge regression algo-rithm is proposed. It uses the aggregating algorithm (AA) to devise an iterative version of ridge regression (IRR). This algorithm is called AAIRR. A competitive analysis is conducted to show that the guarantee on the performance of AAIRR is better than that of the known online ridge regression algorithms. Moreover, an empirical study is carried out on real-world datasets to demonstrate the superior performance over those state-of-the-art algorithms.(c) 2022 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY license ( http://***/licenses/by/4.0/ )

关键词： Online machine learning Ridge regression aggregating algorithm Online machine learning Ridge regression aggregating algorithm

来源：评论

学校读者我要写书评

暂无评论

Online Aggregation of Probabilistic Forecasts Based on the Continuous Ranked Probability Score

引用

JOURNAL OF COMMUNICATIONS TECHNOLOGY AND ELECTRONICS 2020年第6期65卷 662-676页

作者： V'yugin, V. V. Trunov, V. G. Kharkevich Inst Informat Transmiss Problems Moscow 127051 Russia

Methods for generating predictions online and in the form of probability distributions of future outcomes are considered. The difference between the probabilistic forecast (probability distribution) and the numerical outcome is measured using the loss function (scoring rule). In practical statistics, the continuous ranked probability score (CRPS) is often used to estimate the discrepancy between probabilistic forecasts and (quantitative) outcomes. The paper considers the case when several competing methods (experts) give their online predictions as distribution functions. An algorithm is proposed for online aggregation of these distribution functions. The performance bounds of the proposed algorithm are obtained in the form of a comparison of the cumulative loss of the algorithm and the loss of expert hypotheses. Unlike existing estimates, the proposed estimates do not depend on time. The results of numerical experiments illustrating the proposed methods are presented.

关键词： prediction with expert advice online decision making regret aggregating algorithm continuous ranked probability score (CRPS) mixable loss function

来源：评论

学校读者我要写书评

暂无评论

Linear programming with online learning

引用

OPERATIONS RESEARCH LETTERS 2007年第5期35卷 612-618页

作者： Levina, Tatsiana Levin, Yuri McGill, Jeff Nediak, Mikhail Queens Univ Sch Business Kingston ON K7L 3N6 Canada

We propose online decision strategies for time-dependent sequences of linear programs which use no distributional and minimal geometric assumptions about the data. These strategies are obtained through Vovk's aggregating algorithm which combines recommendations from a given strategy pool. We establish an average-performance bound for the resulting solution sequence. (C) 2006 Elsevier B.V. All rights reserved.

关键词： linear programming online learning aggregating algorithm

来源：评论

学校读者我要写书评

暂无评论

Trading-network order formation with the help of the aggregation of specialized forecast algorithms

引用

JOURNAL OF COMMUNICATIONS TECHNOLOGY AND ELECTRONICS 2016年第12期61卷 1400-1410页

作者： V'yugin, V. V. Shamsutdinov, A. I. Russian Acad Sci Kharkevich Inst Informat Transmiss Problems Moscow 127051 Russia

The problem concerning the aggregating of the forecasts of specialized expert strategies is examined using the mathematical theory of machine learning. Expert strategies are understood as the algorithms capable of successively predicting the components of a time series in the online mode. The specialized strategies can refrain from predictions at certain time instants-they make forecasts in compliance with the application area of the specific model of an object region forming their basis. An optimal algorithm whereby the forecasts of such expert strategies are aggregated into the single forecast is proposed. The algorithmic optimality consists in that, on average, its total losses are asymptotically less than those of any active prediction strategies on a set of time instants. The uppermost estimated error of the given mixing of predictions, i.e., the regret of aggregating strategies, is determined. The errors are estimated in the worst situation where no assumptions are made about the mechanism underlying the initial data source. The proposed algorithm is tested using the real information on the commodity circulation of a trading network. The numerical results and estimates of the regret are presented.

关键词： online learning aggregating algorithm specialized experts adaptive regret

来源：评论

学校读者我要写书评

暂无评论

Supermartingales in prediction with expert advice

引用

THEORETICAL COMPUTER SCIENCE 2010年第29-30期411卷 2647-2669页

作者： Chernov, Alexey Kalnishkan, Yuri Zhdanov, Fedor Vovk, Vladimir Univ London Comp Learning Res Ctr Dept Comp Sci Egham TW20 0EX Surrey England

The paper applies the method of defensive forecasting, based on the use of game-theoretic supermartingales, to prediction with expert advice. In the traditional setting of a countable number of experts and a finite number of outcomes, the Defensive Forecasting algorithm is very close to the well-known aggregating algorithm. Not only the performance guarantees but also the predictions are the same for these two methods of fundamentally different nature. The paper also discusses a new setting where the experts can give advice conditional on the learner's future decision. Both the algorithms can be adapted to the new setting and give the same performance guarantees as in the traditional setting. Finally, an application of defensive forecasting to a setting with several loss functions is outlined. (C) 2010 Elsevier B.V. All rights reserved.

关键词： Prediction with expert advice Defensive forecasting algorithm aggregating algorithm

来源：评论

学校读者我要写书评

暂无评论

Practical investment with the long-short game

引用

ANNALS OF MATHEMATICS AND ARTIFICIAL INTELLIGENCE 2023年 1-32页

作者： Al-baghdadi, Najim Kalnishkan, Yuri Lindsay, David Lindsay, Sian Univ London Royal Holloway Dept Comp Sci Egham Hill Egham TW20 0EX Surrey England AlgoLabs Ocean House Bracknell RG12 1AX Berkshire England

In this paper we apply methods of prediction with expert advice to real-world foreign exchange trading data with the aim of finding effective investment strategies. We start with the framework of the long-short game, introduced by Vovk and Watkins (1998), and then propose modifications aimed at improving the performance with respect to standard portfolio performance indicators.

关键词： Prediction with expert advice Online learning aggregating algorithm Portfolio optimisation Long-short game Foreign exchange Currency trading

来源：评论

学校读者我要写书评

暂无评论

Anomaly detection with superexperts under delayed feedback

引用

ANNALS OF MATHEMATICS AND ARTIFICIAL INTELLIGENCE 2023年 1-30页

作者： Dzhamtyrova, Raisa Maple, Carsten Univ London Ctr Reliable Machine Learning London England Univ London Dept Comp Sci Royal Holloway London England Univ Warwick Alan Turing Inst Warwick England Univ Warwick WMG Cyber Secur Ctr Warwick England

The increasing connectivity of data and cyber-physical systems has resulted in a growing number of cyber-attacks. Real-time detection of such attacks, through the identification of anomalous activity, is required so that mitigation and contingent actions can be effectively and rapidly deployed. We propose a new approach for aggregating unsupervised anomaly detection algorithms incorporating feedback when it becomes available. We apply this approach to open-source real datasets and show that both aggregating models, which we call experts, and incorporating feedback significantly improve performance. An important property of the proposed approaches is their theoretical guarantees that they perform close to the best superexpert, which can switch between the best performing experts, in terms of the cumulative average losses.

关键词： Anomaly detection Online learning Prediction with expert advice aggregating algorithm

来源：评论

学校读者我要写书评

暂无评论

Binary switch portfolio

引用

QUANTITATIVE FINANCE 2017年第5期17卷 763-780页

作者： Li, Tengfei Chen, Kani Feng, Yang Ying, Zhiliang Chinese Acad Sci Acad Math & Syst Sci Beijing Peoples R China Shanghai Ctr Math Sci Shanghai Peoples R China Hong Kong Univ Sci & Technol Dept Math Hong Kong Hong Kong Peoples R China Columbia Univ Dept Stat New York NY 10027 USA

We propose herein a new portfolio selection method that switches between two distinct asset allocation strategies. An important component is a carefully designed adaptive switching rule, which is based on a machine learning algorithm. It is shown that using this adaptive switching strategy, the combined wealth of the new approach is a weighted average of that of the successive constant rebalanced portfolio and that of the 1/N portfolio. In particular, it is asymptotically superior to the 1/N portfolio under mild conditions in the long run. Applications to real data show that both the returns and the Sharpe ratios of the proposed binary switch portfolio are the best among several popular competing methods over varying time horizons and stock pools.

关键词： aggregating algorithm Asset return Bayesian analysis Portfolio selection Supervised learning Universal portfolio

来源：评论

学校读者我要写书评

暂无评论

Predicting nearly as well as the best pruning of a decision tree through dynamic programming scheme

引用

THEORETICAL COMPUTER SCIENCE 2001年第1期261卷 179-209页

作者： Takimoto, E Maruoka, A Vovk, V Tohoku Univ Grad Sch Informat Sci Sendai Miyagi 9808579 Japan Univ London Royal Holloway & Bedford New Coll Dept Comp Sci Egham TW20 0EX Surrey England

Helmbold and Schapire gave an on-line prediction algorithm that, when given an unpruned decision tree, produces predictions not much worse than the predictions made by the best pruning of the given decision tree. In this paper, we give two new on-line algorithms. The first algorithm is based on the observation that finding the best pruning can be efficiently solved by a dynamic programming in the "batch" setting where all the data to be predicted are given in advance. This algorithm works well for a wide class of Loss functions, whereas the one given by Helmbold and Schapire is only described for the absolute loss function. Moreover, the algorithm given in this paper is so simple and general that it could be applied to many other on-line optimization problems solved by dynamic programming. We also explore the second algorithm that is competitive not only with the best pruning but also with the best prediction values which are associated with nodes in the decision tree. In this setting, a greatly simplified algorithm is given for the absolute loss function. It can be easily generalized to the case where, instead of using decision trees, data are classified in some arbitrarily fixed manner. (C) 2001 Elsevier Science B.V. All rights reserved.

关键词： on-line prediction pruning decision tree expert model aggregating algorithm

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：