2016 IEEE International Conference on Data Science and Advanced Analytics (DSAA)最新文献_第7页

On the Use of Ontology as A Priori Knowledge into Constrained Clustering 本体作为先验知识在约束聚类中的应用

2016 IEEE International Conference on Data Science and Advanced Analytics (DSAA) Pub Date : 2016-10-01 DOI: 10.1109/DSAA.2016.72

Hatim Chahdi, Nistor Grozavu, I. Mougenot, Laure Berti-Équille, Younès Bennani

引用次数: 4

Causal Structure Learning with Reduced Partial Correlation Thresholding 减少部分相关阈值的因果结构学习

2016 IEEE International Conference on Data Science and Advanced Analytics (DSAA) Pub Date : 2016-10-01 DOI: 10.1109/DSAA.2016.68

A. Sondhi, A. Shojaie

引用次数: 0

Impact of Query Sample Selection Bias on Information Retrieval System Ranking 查询样本选择偏差对信息检索系统排序的影响

2016 IEEE International Conference on Data Science and Advanced Analytics (DSAA) Pub Date : 2016-10-01 DOI: 10.1109/DSAA.2016.43

M. Melucci

引用次数: 7

Trend Detection Based Regret Minimization for Bandit Problems 基于趋势检测的强盗问题后悔最小化

2016 IEEE International Conference on Data Science and Advanced Analytics (DSAA) Pub Date : 2016-10-01 DOI: 10.1109/DSAA.2016.35

Paresh Nakhe, Rebecca Reiffenhäuser

{"title":"Trend Detection Based Regret Minimization for Bandit Problems","authors":"Paresh Nakhe, Rebecca Reiffenhäuser","doi":"10.1109/DSAA.2016.35","DOIUrl":"https://doi.org/10.1109/DSAA.2016.35","url":null,"abstract":"We study a variation of the classical multi-armed bandits problem. In this problem, the learner has to make a sequence of decisions, picking from a fixed set of choices. In each round, she receives as feedback only the loss incurred from the chosen action. Conventionally, this problem has been studied when losses of the actions are drawn from an unknown distribution or when they are adversarial. In this paper, we study this problem when the losses of the actions also satisfy certain structural properties, and especially, do show a trend structure. When this is true, we show that using trend detection, we can achieve regret of order Õ (N √TK) with respect to a switching strategy for the version of the problem where a single action is chosen in each round and Õ (Nm √TK) when m actions are chosen each round. This guarantee is a significant improvement over the conventional benchmark. Our approach can, as a framework, be applied in combination with various well-known bandit algorithms, like Exp3. For both versions of the problem, we give regret guarantees also for the anytime setting, i.e. when length of the choice-sequence is not known in advance. Finally, we pinpoint the advantages of our method by comparing it to some well-known other strategies.","PeriodicalId":193885,"journal":{"name":"2016 IEEE International Conference on Data Science and Advanced Analytics (DSAA)","volume":"112 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124125434","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

The Uniqueness and Greedy Method for Quadratic Compressive Sensing 二次压缩感知的唯一性和贪心方法

2016 IEEE International Conference on Data Science and Advanced Analytics (DSAA) Pub Date : 2016-10-01 DOI: 10.1109/DSAA.2016.94

Jun Fan, Lingchen Kong, Liqun Wang, N. Xiu

引用次数: 0

An Exploratory Statistical Cusp Catastrophe Model 一个探索性的统计尖点突变模型

2016 IEEE International Conference on Data Science and Advanced Analytics (DSAA) Pub Date : 2016-10-01 DOI: 10.1109/DSAA.2016.17

D. Chen, X. Chen, Kai Zhang

{"title":"An Exploratory Statistical Cusp Catastrophe Model","authors":"D. Chen, X. Chen, Kai Zhang","doi":"10.1109/DSAA.2016.17","DOIUrl":"https://doi.org/10.1109/DSAA.2016.17","url":null,"abstract":"The Cusp Catastrophe Model provides a promising approach for health and behavioral researchers to investigate both continuous and quantum changes in one modeling framework. However, application of the model is hindered by unresolved issues around a statistical model fitting to the data. This paper reports our exploratory work in developing a new approach to statistical cusp catastrophe modeling. In this new approach, the Cusp Catastrophe Model is cast into a statistical nonlinear regression for parameter estimation. The algorithms of the delayed convention and Maxwell convention are applied to obtain parameter estimates using maximum likelihood estimation. Through a series of simulation studies, we demonstrate that (a) parameter estimation of this statistical cusp model is unbiased, and (b) use of a bootstrapping procedure enables efficient statistical inference. To test the utility of this new method, we analyze survey data collected for an NIH-funded project providing HIV-prevention education to adolescents in the Bahamas. We found that the results can be more reasonably explained by our approach than other existing methods. Additional research is needed to establish this new approach as the most reliable method for fitting the cusp catastrophe model. Further research should focus on additional theoretical analysis, extension of the model for analyzing categorical and counting data, and additional applications in analyzing different data types.","PeriodicalId":193885,"journal":{"name":"2016 IEEE International Conference on Data Science and Advanced Analytics (DSAA)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130345474","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 5

Robust Online Time Series Prediction with Recurrent Neural Networks 基于循环神经网络的鲁棒在线时间序列预测

2016 IEEE International Conference on Data Science and Advanced Analytics (DSAA) Pub Date : 2016-10-01 DOI: 10.1109/DSAA.2016.92

T. Guo, Zhao Xu, X. Yao, Hai-Ming Chen, K. Aberer, K. Funaya

引用次数: 121

Online Collaborative Prediction of Regional Vote Results 区域投票结果的在线协同预测

2016 IEEE International Conference on Data Science and Advanced Analytics (DSAA) Pub Date : 2016-10-01 DOI: 10.1109/DSAA.2016.31

Vincent Etter, M. E. Khan, M. Grossglauser, Patrick Thiran

{"title":"Online Collaborative Prediction of Regional Vote Results","authors":"Vincent Etter, M. E. Khan, M. Grossglauser, Patrick Thiran","doi":"10.1109/DSAA.2016.31","DOIUrl":"https://doi.org/10.1109/DSAA.2016.31","url":null,"abstract":"We consider online predictions of vote results, where regions across a country vote on an issue under discussion. Such online predictions before and during the day of the vote are useful to media agencies, polling institutes, and political parties, e.g., to identify regions that are crucial in determining the national outcome of a vote. We analyze a unique dataset from Switzerland. The dataset contains 281 votes from 2352 regions over a period of 34 years. We make several contributions towards improving online predictions. First, we show that these votes exhibit a bi-clustering of the vote results, i.e., regions that are spatially close tend to vote similarly, and issues that discuss similar topics show similar global voting patterns. Second, we develop models that can exploit this bi-clustering, as well as the features associated with the votes and regions. Third, we show that, when combining vote results and features together, Bayesian methods are essential to obtaining good performance. Our results show that Bayesian methods give better estimates of the hyperparameters than non-Bayesian methods such as cross-validation. The resulting models generalize well to many different tasks, produce robust predictions, and are easily interpretable.","PeriodicalId":193885,"journal":{"name":"2016 IEEE International Conference on Data Science and Advanced Analytics (DSAA)","volume":"83 S366","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132226896","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 3

Hyperparameter Optimization Machines 超参数优化机

2016 IEEE International Conference on Data Science and Advanced Analytics (DSAA) Pub Date : 2016-10-01 DOI: 10.1109/DSAA.2016.12

Martin Wistuba, Nicolas Schilling, L. Schmidt-Thieme

引用次数: 22

Correcting Relational Bias to Improve Classification in Sparsely-Labeled Networks 修正关系偏差以改善稀疏标记网络的分类

2016 IEEE International Conference on Data Science and Advanced Analytics (DSAA) Pub Date : 2016-10-01 DOI: 10.1109/DSAA.2016.11

J. R. King, Luke K. McDowell

{"title":"Correcting Relational Bias to Improve Classification in Sparsely-Labeled Networks","authors":"J. R. King, Luke K. McDowell","doi":"10.1109/DSAA.2016.11","DOIUrl":"https://doi.org/10.1109/DSAA.2016.11","url":null,"abstract":"Many classification problems involve nodes that have a natural connection between them, such as links between people, pages, or social network accounts. Recent work has demonstrated how to learn relational dependencies from these links, then leverage them as predictive features. However, while this can often improve accuracy, the use of linked information can also lead to cascading prediction errors, especially in the common-case when a network is only sparsely-labeled. In response, this paper examines several existing and new methods for correcting the \"relational bias\" that leads to such errors. First, we explain how existing approaches can be divided into \"resemblance-based\" and \"assignment-based\" methods, and provide the first experimental comparison between them. We demonstrate that all of these methods can improve accuracy, but that the former type typically leads to better accuracy. Moreover, we show that the more flexible methods typically perform best, motivating a new assignment-based method that often improves accuracy vs. a more rigid method. In addition, we demonstrate for the first time that some of these methods can also improve accuracy when combined with Gibbs sampling for inference. However, we show that, with Gibbs, correcting relational bias also requires improving label initialization, and present two new initialization methods that yield large accuracy gains. Finally, we evaluate the effects of relational bias when \"neighbor attributes,\" recently-proposed additions that can provide more stability during inference, are included as model features. We show that such attributes reduce the negative impact of bias, but that using some form of bias correction remains important for achieving maximal accuracy.","PeriodicalId":193885,"journal":{"name":"2016 IEEE International Conference on Data Science and Advanced Analytics (DSAA)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115770200","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0