Proceedings of the twenty-first international conference on Machine learning最新文献_第10页

Efficient hierarchical MCMC for policy search 高效的分层MCMC策略搜索

Proceedings of the twenty-first international conference on Machine learning Pub Date : 2004-07-04 DOI: 10.1145/1015330.1015381

M. Strens

引用次数: 5

Model selection via the AUC 通过AUC进行模型选择

Proceedings of the twenty-first international conference on Machine learning Pub Date : 2004-07-04 DOI: 10.1145/1015330.1015400

Saharon Rosset

引用次数: 105

Relational sequential inference with reliable observations 具有可靠观测值的关系序列推断

Proceedings of the twenty-first international conference on Machine learning Pub Date : 2004-07-04 DOI: 10.1145/1015330.1015420

Alan Fern, R. Givan

引用次数: 5

Learning random walk models for inducing word dependency distributions 学习随机漫步模型来诱导单词依赖分布

Proceedings of the twenty-first international conference on Machine learning Pub Date : 2004-07-04 DOI: 10.1145/1015330.1015442

Kristina Toutanova, Christopher D. Manning, A. Ng

引用次数: 100

Interpolation-based Q-learning Interpolation-based q学习的

Proceedings of the twenty-first international conference on Machine learning Pub Date : 2004-07-04 DOI: 10.1145/1015330.1015445

Csaba Szepesvari, W. Smart

引用次数: 86

Online and batch learning of pseudo-metrics 伪度量的在线和批量学习

Proceedings of the twenty-first international conference on Machine learning Pub Date : 2004-07-04 DOI: 10.1145/1015330.1015376

S. Shalev-Shwartz, Y. Singer, A. Ng

引用次数: 343

K-means clustering via principal component analysis K-means聚类通过主成分分析

Proceedings of the twenty-first international conference on Machine learning Pub Date : 2004-07-04 DOI: 10.1145/1015330.1015408

C. Ding, Xiaofeng He

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引用次数: 1509

Learning associative Markov networks 学习关联马尔可夫网络

Proceedings of the twenty-first international conference on Machine learning Pub Date : 2004-07-04 DOI: 10.1145/1015330.1015444

B. Taskar, Vassil Chatalbashev, D. Koller

{"title":"Learning associative Markov networks","authors":"B. Taskar, Vassil Chatalbashev, D. Koller","doi":"10.1145/1015330.1015444","DOIUrl":"https://doi.org/10.1145/1015330.1015444","url":null,"abstract":"Markov networks are extensively used to model complex sequential, spatial, and relational interactions in fields as diverse as image processing, natural language analysis, and bioinformatics. However, inference and learning in general Markov networks is intractable. In this paper, we focus on learning a large subclass of such models (called associative Markov networks) that are tractable or closely approximable. This subclass contains networks of discrete variables with K labels each and clique potentials that favor the same labels for all variables in the clique. Such networks capture the \"guilt by association\" pattern of reasoning present in many domains, in which connected (\"associated\") variables tend to have the same label. Our approach exploits a linear programming relaxation for the task of finding the best joint assignment in such networks, which provides an approximate quadratic program (QP) for the problem of learning a margin-maximizing Markov network. We show that for associative Markov network over binary-valued variables, this approximate QP is guaranteed to return an optimal parameterization for Markov networks of arbitrary topology. For the nonbinary case, optimality is not guaranteed, but the relaxation produces good solutions in practice. Experimental results with hypertext and newswire classification show significant advantages over standard approaches.","PeriodicalId":159792,"journal":{"name":"Proceedings of the twenty-first international conference on Machine learning","volume":"92 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2004-07-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126196644","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: 201

Hyperplane margin classifiers on the multinomial manifold 多项式流形上的超平面边缘分类器

Proceedings of the twenty-first international conference on Machine learning Pub Date : 2004-07-04 DOI: 10.1145/1015330.1015333

Guy Lebanon, J. Lafferty

引用次数: 34

Leveraging the margin more carefully 更谨慎地利用保证金

Proceedings of the twenty-first international conference on Machine learning Pub Date : 2004-07-04 DOI: 10.1145/1015330.1015344

Nir Krause, Y. Singer

引用次数: 92