... International Conference on Big Data and Smart Computing. International Conference on Big Data and Smart Computing最新文献

Reducing Hadoop 3.x energy consumption through Energy Efficient Ethernet 减少Hadoop 3。x的能源消耗通过节能以太网

... International Conference on Big Data and Smart Computing. International Conference on Big Data and Smart Computing Pub Date : 2022-01-01 DOI: 10.1109/BigComp54360.2022.00012

Jorgi Luiz Bolonhezi Dias, Leandro Batista de Almeida, L. Albini

引用次数: 2

Minipatch Learning as Implicit Ridge-Like Regularization. 隐式脊状正则化的Minipatch学习。

... International Conference on Big Data and Smart Computing. International Conference on Big Data and Smart Computing Pub Date : 2021-01-01 Epub Date: 2021-03-10 DOI: 10.1109/bigcomp51126.2021.00021

Tianyi Yao, Daniel LeJeune, Hamid Javadi, Richard G Baraniuk, Genevera I Allen

{"title":"Minipatch Learning as Implicit Ridge-Like Regularization.","authors":"Tianyi Yao, Daniel LeJeune, Hamid Javadi, Richard G Baraniuk, Genevera I Allen","doi":"10.1109/bigcomp51126.2021.00021","DOIUrl":"https://doi.org/10.1109/bigcomp51126.2021.00021","url":null,"abstract":"Ridge-like regularization often leads to improved generalization performance of machine learning models by mitigating overfitting. While ridge-regularized machine learning methods are widely used in many important applications, direct training via optimization could become challenging in huge data scenarios with millions of examples and features. We tackle such challenges by proposing a general approach that achieves ridge-like regularization through implicit techniques named Minipatch Ridge (MPRidge). Our approach is based on taking an ensemble of coefficients of unregularized learners trained on many tiny, random subsamples of both the examples and features of the training data, which we call minipatches. We empirically demonstrate that MPRidge induces an implicit ridge-like regularizing effect and performs nearly the same as explicit ridge regularization for a general class of predictors including logistic regression, SVM, and robust regression. Embarrassingly parallelizable, MPRidge provides a computationally appealing alternative to inducing ridge-like regularization for improving generalization performance in challenging big-data settings.","PeriodicalId":93400,"journal":{"name":"... International Conference on Big Data and Smart Computing. International Conference on Big Data and Smart Computing","volume":"2021 ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1109/bigcomp51126.2021.00021","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"39865226","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 9

MP-Boost: Minipatch Boosting via Adaptive Feature and Observation Sampling. MP-Boost:通过自适应特征和观察采样的小补丁增强。

... International Conference on Big Data and Smart Computing. International Conference on Big Data and Smart Computing Pub Date : 2021-01-01 Epub Date: 2021-03-10 DOI: 10.1109/bigcomp51126.2021.00023

Mohammad Taha Toghani, Genevera I Allen

{"title":"MP-Boost: Minipatch Boosting via Adaptive Feature and Observation Sampling.","authors":"Mohammad Taha Toghani, Genevera I Allen","doi":"10.1109/bigcomp51126.2021.00023","DOIUrl":"https://doi.org/10.1109/bigcomp51126.2021.00023","url":null,"abstract":"Boosting methods are among the best general-purpose and off-the-shelf machine learning approaches, gaining widespread popularity. In this paper, we seek to develop a boosting method that yields comparable accuracy to popular AdaBoost and gradient boosting methods, yet is faster computationally and whose solution is more interpretable. We achieve this by developing MP-Boost, an algorithm loosely based on AdaBoost that learns by adaptively selecting small subsets of instances and features, or what we term minipatches (MP), at each iteration. By sequentially learning on tiny subsets of the data, our approach is computationally faster than other classic boosting algorithms. Also as it progresses, MP-Boost adaptively learns a probability distribution on the features and instances that upweight the most important features and challenging instances, hence adaptively selecting the most relevant minipatches for learning. These learned probability distributions also aid in interpretation of our method. We empirically demonstrate the interpretability, comparative accuracy, and computational time of our approach on a variety of binary classification tasks.","PeriodicalId":93400,"journal":{"name":"... International Conference on Big Data and Smart Computing. International Conference on Big Data and Smart Computing","volume":"2021 ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1109/bigcomp51126.2021.00023","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"39677761","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Solid-State LiDAR based-SLAM: A Concise Review and Application 基于slam的固态激光雷达:综述与应用

... International Conference on Big Data and Smart Computing. International Conference on Big Data and Smart Computing Pub Date : 2021-01-01 DOI: 10.1109/BigComp51126.2021.00064

N. Dinh, Gon-Woo Kim

引用次数: 4

Anhang E: Anwendungsbeispiel 1 附录E

... International Conference on Big Data and Smart Computing. International Conference on Big Data and Smart Computing Pub Date : 2019-12-31 DOI: 10.1515/9783110618372-016

引用次数: 0

Abstract 摘要

... International Conference on Big Data and Smart Computing. International Conference on Big Data and Smart Computing Pub Date : 2019-12-31 DOI: 10.1515/9783110618372-002

Numărul, Secţia, Construcţii DE Maşini

引用次数: 0

6. Anwendungsbeispiele

... International Conference on Big Data and Smart Computing. International Conference on Big Data and Smart Computing Pub Date : 2019-12-31 DOI: 10.1515/9783110618372-008

引用次数: 0

Frontmatter

... International Conference on Big Data and Smart Computing. International Conference on Big Data and Smart Computing Pub Date : 2019-12-31 DOI: 10.1515/9783110618372-fm

引用次数: 0

5. Vorgehen der praktischen Anwendung 5. 实际应用程序

... International Conference on Big Data and Smart Computing. International Conference on Big Data and Smart Computing Pub Date : 2019-12-31 DOI: 10.1515/9783110618372-007

引用次数: 0

Anhang C: Katalog der Anwendungsziele 附录C:应用目标目录

... International Conference on Big Data and Smart Computing. International Conference on Big Data and Smart Computing Pub Date : 2019-12-31 DOI: 10.1515/9783110618372-014

引用次数: 0