Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining最新文献_第5页

Grafting-light: fast, incremental feature selection and structure learning of Markov random fields 嫁接轻:快速，增量特征选择和马尔可夫随机场的结构学习

Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining Pub Date : 2010-07-25 DOI: 10.1145/1835804.1835845

Jun Zhu, N. Lao, E. Xing

{"title":"Grafting-light: fast, incremental feature selection and structure learning of Markov random fields","authors":"Jun Zhu, N. Lao, E. Xing","doi":"10.1145/1835804.1835845","DOIUrl":"https://doi.org/10.1145/1835804.1835845","url":null,"abstract":"Feature selection is an important task in order to achieve better generalizability in high dimensional learning, and structure learning of Markov random fields (MRFs) can automatically discover the inherent structures underlying complex data. Both problems can be cast as solving an l1-norm regularized parameter estimation problem. The existing Grafting method can avoid doing inference on dense graphs in structure learning by incrementally selecting new features. However, Grafting performs a greedy step to optimize over free parameters once new features are included. This greedy strategy results in low efficiency when parameter learning is itself non-trivial, such as in MRFs, in which parameter learning depends on an expensive subroutine to calculate gradients. The complexity of calculating gradients in MRFs is typically exponential to the size of maximal cliques. In this paper, we present a fast algorithm called Grafting-Light to solve the l1-norm regularized maximum likelihood estimation of MRFs for efficient feature selection and structure learning. Grafting-Light iteratively performs one-step of orthant-wise gradient descent over free parameters and selects new features. This lazy strategy is guaranteed to converge to the global optimum and can effectively select significant features. On both synthetic and real data sets, we show that Grafting-Light is much more efficient than Grafting for both feature selection and structure learning, and performs comparably with the optimal batch method that directly optimizes over all the features for feature selection but is much more efficient and accurate for structure learning of MRFs.","PeriodicalId":20529,"journal":{"name":"Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining","volume":"35 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2010-07-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"88594401","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: 40

Session details: Research track 5: classification models and tools 专题研究5:分类模型和工具

Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining Pub Date : 2010-07-25 DOI: 10.1145/3248785

Gregory Piatetsky

引用次数: 0

An energy-efficient mobile recommender system 高效节能的移动推荐系统

Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining Pub Date : 2010-07-25 DOI: 10.1145/1835804.1835918

Yong Ge, Hui Xiong, A. Tuzhilin, Keli Xiao, M. Gruteser, M. Pazzani

{"title":"An energy-efficient mobile recommender system","authors":"Yong Ge, Hui Xiong, A. Tuzhilin, Keli Xiao, M. Gruteser, M. Pazzani","doi":"10.1145/1835804.1835918","DOIUrl":"https://doi.org/10.1145/1835804.1835918","url":null,"abstract":"The increasing availability of large-scale location traces creates unprecedent opportunities to change the paradigm for knowledge discovery in transportation systems. A particularly promising area is to extract energy-efficient transportation patterns (green knowledge), which can be used as guidance for reducing inefficiencies in energy consumption of transportation sectors. However, extracting green knowledge from location traces is not a trivial task. Conventional data analysis tools are usually not customized for handling the massive quantity, complex, dynamic, and distributed nature of location traces. To that end, in this paper, we provide a focused study of extracting energy-efficient transportation patterns from location traces. Specifically, we have the initial focus on a sequence of mobile recommendations. As a case study, we develop a mobile recommender system which has the ability in recommending a sequence of pick-up points for taxi drivers or a sequence of potential parking positions. The goal of this mobile recommendation system is to maximize the probability of business success. Along this line, we provide a Potential Travel Distance (PTD) function for evaluating each candidate sequence. This PTD function possesses a monotone property which can be used to effectively prune the search space. Based on this PTD function, we develop two algorithms, LCP and SkyRoute, for finding the recommended routes. Finally, experimental results show that the proposed system can provide effective mobile sequential recommendation and the knowledge extracted from location traces can be used for coaching drivers and leading to the efficient use of energy.","PeriodicalId":20529,"journal":{"name":"Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining","volume":"11 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2010-07-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77045857","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: 369

Combined regression and ranking 组合回归与排序

Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining Pub Date : 2010-07-25 DOI: 10.1145/1835804.1835928

D. Sculley

引用次数: 155

Dynamics of conversations 对话的动态

Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining Pub Date : 2010-07-25 DOI: 10.1145/1835804.1835875

Ravi Kumar, Mohammad Mahdian, Mary McGlohon

引用次数: 130

Session details: Research track 10: graph mining and classification 会议细节:研究专题10:图挖掘和分类

Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining Pub Date : 2010-07-25 DOI: 10.1145/3248790

J. Pei

引用次数: 0

Feature selection for support vector regression using probabilistic prediction 基于概率预测的支持向量回归特征选择

Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining Pub Date : 2010-07-25 DOI: 10.1145/1835804.1835849

Jian-Bo Yang, C. Ong

引用次数: 12

Privacy-preserving outsourcing support vector machines with random transformation 具有随机变换的隐私保护外包支持向量机

Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining Pub Date : 2010-07-25 DOI: 10.1145/1835804.1835852

Keng-Pei Lin, Ming-Syan Chen

引用次数: 47

Evaluating online ad campaigns in a pipeline: causal models at scale 评估在线广告活动:大规模的因果模型

Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining Pub Date : 2010-07-25 DOI: 10.1145/1835804.1835809

David Chan, Rong Ge, Ori Gershony, Tim Hesterberg, D. Lambert

引用次数: 110

Multi-task learning for boosting with application to web search ranking 多任务学习提高与应用程序的网页搜索排名

Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining Pub Date : 2010-07-25 DOI: 10.1145/1835804.1835953

O. Chapelle, Pannagadatta K. Shivaswamy, Srinivas Vadrevu, Kilian Q. Weinberger, Ya Zhang, Belle L. Tseng

引用次数: 111