检索结果-内蒙古大学图书馆

21st Text REtrieval Conference, TREC 2012

作者： Kanoulas, Evangelos Carterette, Ben Hall, Mark Clough, Paul Sanderson, Mark Google Zurich Switzerland Department of Computer & Information Sciences University of Delaware NewarkDE United States Information School University of Sheffield Sheffield United Kingdom Department of Computer Science & Information Technology RMIT University Melbourne Australia

来源：评论

学校读者我要写书评

暂无评论

The rate of convergence of AdaBoost

The Journal of Machine Learning Research

引用

The Journal of Machine Learning Research 2013年第1期14卷

作者： Kevin Murphy Bernhard Schölkopf Indraneel Mukherjee Cynthia Rudin Robert E. Schapire Google MPI for Intelligent Systems Princeton University Department of Computer Science Princeton NJ Massachusetts Institute of Technology MIT Sloan School of Management Cambridge MA

The AdaBoost algorithm was designed to combine many "weak" hypotheses that perform slightly better than random guessing into a "strong" hypothesis that has very low error. We study the rate at which AdaBoost iteratively converges to the minimum of the "exponential loss." Unlike previous work, our proofs do not require a weak-learning assumption, nor do they require that minimizers of the exponential loss are finite. Our first result shows that the exponential loss of AdaBoost's computed parameter vector will be at most e more than that of any parameter vector of l1-norm bounded by B in a number of rounds that is at most a polynomial in B and 1/ε. We also provide lower bounds showing that a polynomial dependence is necessary. Our second result is that within C/ε iterations, AdaBoost achieves a value of the exponential loss that is at most e more than the best possible value, where C depends on the data set. We show that this dependence of the rate on ε is optimal up to constant factors, that is, at least Ω(1/ε) rounds are necessary to achieve within ε of the optimal exponential loss.

关键词： AdaBoost convergence rate coordinate descent optimization

来源：评论

学校读者我要写书评

暂无评论

Convex and scalable weakly labeled SVMs

The Journal of Machine Learning Research

引用

The Journal of Machine Learning Research 2013年第1期14卷

作者： Kevin Murphy Bernhard Schölkopf Yu-Feng Li Ivor W. Tsang James T. Kwok Zhi-Hua Zhou Google MPI for Intelligent Systems National Key Laboratory for Novel Software Technology Nanjing University Nanjing China School of Computer Engineering Nanyang Technological University Singapore Department of Computer Science and Engineering Hong Kong University of Science & Technology Hong Kong

In this paper, we study the problem of learning from weakly labeled data, where labels of the training examples are incomplete. This includes, for example, (i) semi-supervised learning where labels are partially known; (ii) multi-instance learning where labels are implicitly known; and (iii) clustering where labels are completely unknown. Unlike supervised learning, learning with weak labels involves a difficult Mixed-Integer Programming (MIP) problem. Therefore, it can suffer from poor scalability and may also get stuck in local minimum. In this paper, we focus on SVMs and propose the WELLSVM via a novel label generation strategy. This leads to a convex relaxation of the original MIP, which is at least as tight as existing convex Semi-Definite Programming (SDP) relaxations. Moreover, the WELLSVM can be solved via a sequence of SVM subproblems that are much more scalable than previous convex SDP relaxations. Experiments on three weakly labeled learning tasks, namely, (i) semi-supervised learning; (ii) multi-instance learning for locating regions of interest in content-based information retrieval; and (iii) clustering, clearly demonstrate improved performance, and WELLSVM is also readily applicable on large data sets.

关键词： clustering convex relaxation cutting plane multi-instance learning semi-supervised learning weakly labeled data

来源：评论

学校读者我要写书评

暂无评论

Experiment selection for causal discovery

The Journal of Machine Learning Research

引用

The Journal of Machine Learning Research 2013年第1期14卷

作者： Kevin Murphy Bernhard Schölkopf Antti Hyttinen Frederick Eberhardt Patrik O. Hoyer Google MPI for Intelligent Systems Helsinki Institute for Information Technology Department of Computer Science University of Helsinki Finland Philosophy Division of the Humanities and Social Sciences California Institute of Technology Pasadena CA

Randomized controlled experiments are often described as the most reliable tool available to scientists for discovering causal relationships among quantities of interest. However, it is often unclear how many and which different experiments are needed to identify the full (possibly cyclic) causal structure among some given (possibly causally insufficient) set of variables. Recent results in the causal discovery literature have explored various identifiability criteria that depend on the assumptions one is able to make about the underlying causal process, but these criteria are not directly constructive for selecting the optimal set of experiments. Fortunately, many of the needed constructions already exist in the combinatorics literature, albeit under terminology which is unfamiliar to most of the causal discovery community. In this paper we translate the theoretical results and apply them to the concrete problem of experiment selection. For a variety of settings we give explicit constructions of the optimal set of experiments and adapt some of the general combinatorics results to answer questions relating to the problem of experiment selection.

关键词： causality completely separating systems cut-coverings experiment selection randomized experiments separating systems

来源：评论

学校读者我要写书评

暂无评论

Machine learning with operational costs

The Journal of Machine Learning Research

引用

The Journal of Machine Learning Research 2013年第1期14卷

作者： Kevin Murphy Bernhard Schölkopf Theja Tulabandhula Cynthia Rudin Google MPI for Intelligent Systems Department of Electrical Engineering and Computer Science Massachusetts Institute of Technology Cambridge MA MIT Sloan School of Management and Operations Research Center Massachusetts Institute of Technology Cambridge MA

This work proposes a way to align statistical modeling with decision making. We provide a method that propagates the uncertainty in predictive modeling to the uncertainty in operational cost, where operational cost is the amount spent by the practitioner in solving the problem. The method allows us to explore the range of operational costs associated with the set of reasonable statistical models, so as to provide a useful way for practitioners to understand uncertainty. To do this, the operational cost is cast as a regularization term in a learning algorithm's objective function, allowing either an optimistic or pessimistic view of possible costs, depending on the regularization parameter. From another perspective, if we have prior knowledge about the operational cost, for instance that it should be low, this knowledge can help to restrict the hypothesis space, and can help with generalization. We provide a theoretical generalization bound for this scenario. We also show that learning with operational costs is related to robust optimization.

关键词： covering numbers decision theory optimization statistical learning theory

来源：评论

学校读者我要写书评

暂无评论

Fast and scalable decoding with language model look-ahead for phrase-based statistical machine translation

Fast and scalable decoding with language model look-ahead fo...

引用

50th Annual Meeting of the Association for Computational Linguistics, ACL 2012

作者： Wuebker, Joern Ney, Hermann Zens, Richard Computer Science Department Human Language Technology and Pattern Recognition Group RWTH Aachen University Germany Google Inc. 1600 Amphitheatre Parkway Mountain View CA 94043 United States

ISBN: (纸本)9781937284251

In this work we present two extensions to the well-known dynamic programming beam search in phrase-based statistical machine translation (SMT), aiming at increased efficiency of decoding by minimizing the number of language model computations and hypothesis expansions. Our results show that language model based pre-sorting yields a small improvement in translation quality and a speedup by a factor of 2. Two look-ahead methods are shown to further increase translation speed by a factor of 2 without changing the search space and a factor of 4 with the side-effect of some additional search errors. We compare our approach with Moses and observe the same performance, but a substantially better trade-off between translation quality and speed. At a speed of roughly 70 words per second, Moses reaches 17.2% BLEU, whereas our approach yields 20.0% with identical models. © 2012 Association for Computational Linguistics.

关键词： Decoding

来源：评论

学校读者我要写书评

暂无评论

Maximum volume clustering: a new discriminative clustering approach

The Journal of Machine Learning Research

引用

The Journal of Machine Learning Research 2013年第1期14卷

作者： Kevin Murphy Bernhard Schölkopf Gang Niu Bo Dai Lin Shang Masashi Sugiyama Google MPI for Intelligent Systems Department of Computer Science Tokyo Institute of Technology Tokyo Japan College of Computing Georgia Institute of Technology Atlanta GA State Key Laboratory for Novel Software Technology Nanjing University Nanjing China

The large volume principle proposed by Vladimir Vapnik, which advocates that hypotheses lying in an equivalence class with a larger volume are more preferable, is a useful alternative to the large margin principle. In this paper, we introduce a new discriminative clustering model based on the large volume principle called maximum volume clustering (MVC), and then propose two approximation schemes to solve this MVC model: A soft-label MVC method using sequential quadratic programming and a hard-label MVC method using semi-definite programming, respectively. The proposed MVC is theoretically advantageous for three reasons. The optimization involved in hard-label MVC is convex, and under mild conditions, the optimization involved in soft-label MVC is akin to a convex one in terms of the resulting clusters. Secondly, the soft-label MVC method possesses a clustering error bound. Thirdly, MVC includes the optimization problems of a spectral clustering, two relaxed k-means clustering and an information-maximization clustering as special limit cases when its regularization parameter goes to infinity. Experiments on several artificial and benchmark data sets demonstrate that the proposed MVC compares favorably with state-of-the-art clustering methods.

关键词： clustering error bound discriminative clustering finite sample stability large volume principle semi-definite programming sequential quadratic programming

来源：评论

学校读者我要写书评

暂无评论

Multi-stage multi-task feature learning

The Journal of Machine Learning Research

引用

The Journal of Machine Learning Research 2013年第1期14卷

作者： Kevin Murphy Bernhard Schölkopf Pinghua Gong Jieping Ye Changshui Zhang Google MPI for Intelligent Systems State Key Laboratory on Intelligent Technology and Systems Tsinghua National Laboratory for Information Science and Technology Department of Automation Tsinghua University Beijing China Computer Science and Engineering Center for Evolutionary Medicine and Informatics The Biodesign Institute Arizona State University Tempe AZ

Multi-task sparse feature learning aims to improve the generalization performance by exploiting the shared features among tasks. It has been successfully applied to many applications including computer vision and biomedical informatics. Most of the existing multi-task sparse feature learning algorithms are formulated as a convex sparse regularization problem, which is usually suboptimal, due to its looseness for approximating an l0-type regularizer. In this paper, we propose a non-convex formulation for multi-task sparse feature learning based on a novel nonconvex regularizer. To solve the non-convex optimization problem, we propose a Multi-Stage Multi-Task Feature Learning (MSMTFL) algorithm; we also provide intuitive interpretations, detailed convergence and reproducibility analysis for the proposed algorithm. Moreover, we present a detailed theoretical analysis showing that MSMTFL achieves a better parameter estimation error bound than the convex formulation. Empirical studies on both synthetic and real-world data sets demonstrate the effectiveness of MSMTFL in comparison with the state of the art multi-task sparse feature learning algorithms.

关键词： multi-stage multi-task learning non-convex sparse learning

来源：评论

学校读者我要写书评

暂无评论

Error Modeling and Estimation Fusion for Indoor Localization

Error Modeling and Estimation Fusion for Indoor Localization

引用

IEEE International Conference on Multimedia and Expo (ICME)

作者： Weipeng Zhuo Bo Zhang S.H. Gary Chan Edward Y. Chang Department of Computer Science and Engineering Hong Kong University of Science and Technology Hong Kong China Google Inc. Beijing China

ISBN: (纸本)9781467316590

There has been much interest in offering multimedia location-based service (LBS) to indoor users (e.g., sending video/audio streams according to user locations). Offering good LBS largely depends on accurate indoor localization of mobile stations (MSs). To achieve that, in this paper we first model and analyze the error characteristics of important indoor localization schemes, using Radio Frequency Identification (RFID) and Wi-Fi. Our models are simple to use, capturing important system parameters and measurement noises, and quantifying how they affect the accuracies of the localization. Given that there have been many indoor localization techniques deployed, an MS may receive simultaneously multiple co-existing estimations on its location. Equipped with the understanding of location errors, we then investigate how to optimally combine, or fuse, all the co-existing estimations of an MS's location. We present computationally-efficient closed-form expressions to fuse the outputs of the estimators. Simulation and experimental results show that our fusion technique achieves higher location accuracy in spite of location errors in the estimators.

关键词： Estimation IEEE 802.11 Standards Radiofrequency identification Accuracy Analytical models Noise Noise measurement

来源：评论

学校读者我要写书评

暂无评论

Global analytic solution of fully-observed variational Bayesian matrix factorization

The Journal of Machine Learning Research

引用

The Journal of Machine Learning Research 2013年第1期14卷

作者： Kevin Murphy Bernhard Schölkopf Shinichi Nakajima Masashi Sugiyama S. Derin Babacan Ryota Tomioka Google MPI for Intelligent Systems Optical Research Laboratory Nikon Corporation Tokyo Japan Department of Computer Science Tokyo Institute of Technology Tokyo Japan Beckman Institute University of Illinois at Urbana-Champaign Urbana IL Department of Mathematical Informatics The University of Tokyo Tokyo Japan

The variational Bayesian (VB) approximation is known to be a promising approach to Bayesian estimation, when the rigorous calculation of the Bayes posterior is intractable. The VB approximation has been successfully applied to matrix factorization (MF), offering automatic dimensionality selection for principal component analysis. Generally, finding the VB solution is a nonconvex problem, and most methods rely on a local search algorithm derived through a standard procedure for the VB approximation. In this paper, we show that a better option is available for fully-observed VBMF--the global solution can be analytically computed. More specifically, the global solution is a reweighted SVD of the observed matrix, and each weight can be obtained by solving a quartic equation with its coefficients being functions of the observed singular value. We further show that the global optimal solution of empirical VBMF (where hyperparameters are also learned from data) can also be analytically computed. We illustrate the usefulness of our results through experiments in multi-variate analysis.

关键词： empirical bayes matrix factorization model-induced regularization probabilistic PCA variational bayes

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：