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Proceedings of the 23rd international conference on Machine learning最新文献

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A DC-programming algorithm for kernel selection 核选择的一种dc编程算法
Proceedings of the 23rd international conference on Machine learning Pub Date : 2006-06-25 DOI: 10.1145/1143844.1143850
Andreas Argyriou, Raphael Andreas Hauser, C. Micchelli, M. Pontil
{"title":"A DC-programming algorithm for kernel selection","authors":"Andreas Argyriou, Raphael Andreas Hauser, C. Micchelli, M. Pontil","doi":"10.1145/1143844.1143850","DOIUrl":"https://doi.org/10.1145/1143844.1143850","url":null,"abstract":"We address the problem of learning a kernel for a given supervised learning task. Our approach consists in searching within the convex hull of a prescribed set of basic kernels for one which minimizes a convex regularization functional. A unique feature of this approach compared to others in the literature is that the number of basic kernels can be infinite. We only require that they are continuously parameterized. For example, the basic kernels could be isotropic Gaussians with variance in a prescribed interval or even Gaussians parameterized by multiple continuous parameters. Our work builds upon a formulation involving a minimax optimization problem and a recently proposed greedy algorithm for learning the kernel. Although this optimization problem is not convex, it belongs to the larger class of DC (difference of convex functions) programs. Therefore, we apply recent results from DC optimization theory to create a new algorithm for learning the kernel. Our experimental results on benchmark data sets show that this algorithm outperforms a previously proposed method.","PeriodicalId":124011,"journal":{"name":"Proceedings of the 23rd international conference on Machine learning","volume":"31 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2006-06-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125759454","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: 123
Locally adaptive classification piloted by uncertainty 基于不确定性的局部自适应分类
Proceedings of the 23rd international conference on Machine learning Pub Date : 2006-06-25 DOI: 10.1145/1143844.1143873
J. Dai, Shuicheng Yan, Xiaoou Tang, J. Kwok
{"title":"Locally adaptive classification piloted by uncertainty","authors":"J. Dai, Shuicheng Yan, Xiaoou Tang, J. Kwok","doi":"10.1145/1143844.1143873","DOIUrl":"https://doi.org/10.1145/1143844.1143873","url":null,"abstract":"Locally adaptive classifiers are usually superior to the use of a single global classifier. However, there are two major problems in designing locally adaptive classifiers. First, how to place the local classifiers, and, second, how to combine them together. In this paper, instead of placing the classifiers based on the data distribution only, we propose a responsibility mixture model that uses the uncertainty associated with the classification at each training sample. Using this model, the local classifiers are placed near the decision boundary where they are most effective. A set of local classifiers are then learned to form a global classifier by maximizing an estimate of the probability that the samples will be correctly classified with a nearest neighbor classifier. Experimental results on both artificial and real-world data sets demonstrate its superiority over traditional algorithms.","PeriodicalId":124011,"journal":{"name":"Proceedings of the 23rd international conference on Machine learning","volume":"35 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2006-06-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132395544","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: 21
R1-PCA: rotational invariant L1-norm principal component analysis for robust subspace factorization R1-PCA:鲁棒子空间分解的旋转不变l1范数主成分分析
Proceedings of the 23rd international conference on Machine learning Pub Date : 2006-06-25 DOI: 10.1145/1143844.1143880
C. Ding, Ding Zhou, Xiaofeng He, H. Zha
{"title":"R1-PCA: rotational invariant L1-norm principal component analysis for robust subspace factorization","authors":"C. Ding, Ding Zhou, Xiaofeng He, H. Zha","doi":"10.1145/1143844.1143880","DOIUrl":"https://doi.org/10.1145/1143844.1143880","url":null,"abstract":"Principal component analysis (PCA) minimizes the sum of squared errors (L2-norm) and is sensitive to the presence of outliers. We propose a rotational invariant L1-norm PCA (R1-PCA). R1-PCA is similar to PCA in that (1) it has a unique global solution, (2) the solution are principal eigenvectors of a robust covariance matrix (re-weighted to soften the effects of outliers), (3) the solution is rotational invariant. These properties are not shared by the L1-norm PCA. A new subspace iteration algorithm is given to compute R1-PCA efficiently. Experiments on several real-life datasets show R1-PCA can effectively handle outliers. We extend R1-norm to K-means clustering and show that L1-norm K-means leads to poor results while R1-K-means outperforms standard K-means.","PeriodicalId":124011,"journal":{"name":"Proceedings of the 23rd international conference on Machine learning","volume":"140 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2006-06-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131560183","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: 645
Quadratic programming relaxations for metric labeling and Markov random field MAP estimation 度量标记和马尔可夫随机场MAP估计的二次规划松弛
Proceedings of the 23rd international conference on Machine learning Pub Date : 2006-06-25 DOI: 10.1145/1143844.1143937
Pradeep Ravikumar, J. Lafferty
{"title":"Quadratic programming relaxations for metric labeling and Markov random field MAP estimation","authors":"Pradeep Ravikumar, J. Lafferty","doi":"10.1145/1143844.1143937","DOIUrl":"https://doi.org/10.1145/1143844.1143937","url":null,"abstract":"Quadratic program relaxations are proposed as an alternative to linear program relaxations and tree reweighted belief propagation for the metric labeling or MAP estimation problem. An additional convex relaxation of the quadratic approximation is shown to have additive approximation guarantees that apply even when the graph weights have mixed sign or do not come from a metric. The approximations are extended in a manner that allows tight variational relaxations of the MAP problem, although they generally involve non-convex optimization. Experiments carried out on synthetic data show that the quadratic approximations can be more accurate and computationally efficient than the linear programming and propagation based alternatives.","PeriodicalId":124011,"journal":{"name":"Proceedings of the 23rd international conference on Machine learning","volume":"24 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2006-06-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132884699","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: 129
Bayesian learning of measurement and structural models 测量和结构模型的贝叶斯学习
Proceedings of the 23rd international conference on Machine learning Pub Date : 2006-06-25 DOI: 10.1145/1143844.1143948
Ricardo Silva, R. Scheines
{"title":"Bayesian learning of measurement and structural models","authors":"Ricardo Silva, R. Scheines","doi":"10.1145/1143844.1143948","DOIUrl":"https://doi.org/10.1145/1143844.1143948","url":null,"abstract":"We present a Bayesian search algorithm for learning the structure of latent variable models of continuous variables. We stress the importance of applying search operators designed especially for the parametric family used in our models. This is performed by searching for subsets of the observed variables whose covariance matrix can be represented as a sum of a matrix of low rank and a diagonal matrix of residuals. The resulting search procedure is relatively efficient, since the main search operator has a branch factor that grows linearly with the number of variables. The resulting models are often simpler and give a better fit than models based on generalizations of factor analysis or those derived from standard hill-climbing methods.","PeriodicalId":124011,"journal":{"name":"Proceedings of the 23rd international conference on Machine learning","volume":"17 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2006-06-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114490165","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: 12
Semi-supervised nonlinear dimensionality reduction 半监督非线性降维
Proceedings of the 23rd international conference on Machine learning Pub Date : 2006-06-25 DOI: 10.1145/1143844.1143978
Xin Yang, Haoying Fu, H. Zha, J. Barlow
{"title":"Semi-supervised nonlinear dimensionality reduction","authors":"Xin Yang, Haoying Fu, H. Zha, J. Barlow","doi":"10.1145/1143844.1143978","DOIUrl":"https://doi.org/10.1145/1143844.1143978","url":null,"abstract":"The problem of nonlinear dimensionality reduction is considered. We focus on problems where prior information is available, namely, semi-supervised dimensionality reduction. It is shown that basic nonlinear dimensionality reduction algorithms, such as Locally Linear Embedding (LLE), Isometric feature mapping (ISOMAP), and Local Tangent Space Alignment (LTSA), can be modified by taking into account prior information on exact mapping of certain data points. The sensitivity analysis of our algorithms shows that prior information will improve stability of the solution. We also give some insight on what kind of prior information best improves the solution. We demonstrate the usefulness of our algorithm by synthetic and real life examples.","PeriodicalId":124011,"journal":{"name":"Proceedings of the 23rd international conference on Machine learning","volume":"21 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2006-06-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117015861","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: 139
Estimating relatedness via data compression 通过数据压缩估计相关性
Proceedings of the 23rd international conference on Machine learning Pub Date : 2006-06-25 DOI: 10.1145/1143844.1143900
Brendan Juba
{"title":"Estimating relatedness via data compression","authors":"Brendan Juba","doi":"10.1145/1143844.1143900","DOIUrl":"https://doi.org/10.1145/1143844.1143900","url":null,"abstract":"We show that it is possible to use data compression on independently obtained hypotheses from various tasks to algorithmically provide guarantees that the tasks are sufficiently related to benefit from multitask learning. We give uniform bounds in terms of the empirical average error for the true average error of the n hypotheses provided by deterministic learning algorithms drawing independent samples from a set of n unknown computable task distributions over finite sets.","PeriodicalId":124011,"journal":{"name":"Proceedings of the 23rd international conference on Machine learning","volume":"33 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2006-06-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116828963","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
Active learning via transductive experimental design 通过转换实验设计进行主动学习
Proceedings of the 23rd international conference on Machine learning Pub Date : 2006-06-25 DOI: 10.1145/1143844.1143980
Kai Yu, J. Bi, Volker Tresp
{"title":"Active learning via transductive experimental design","authors":"Kai Yu, J. Bi, Volker Tresp","doi":"10.1145/1143844.1143980","DOIUrl":"https://doi.org/10.1145/1143844.1143980","url":null,"abstract":"This paper considers the problem of selecting the most informative experiments x to get measurements y for learning a regression model y = f(x). We propose a novel and simple concept for active learning, transductive experimental design, that explores available unmeasured experiments (i.e., unlabeled data) and has a better scalability in comparison with classic experimental design methods. Our in-depth analysis shows that the new method tends to favor experiments that are on the one side hard-to-predict and on the other side representative for the rest of the experiments. Efficient optimization of the new design problem is achieved through alternating optimization and sequential greedy search. Extensive experimental results on synthetic problems and three real-world tasks, including questionnaire design for preference learning, active learning for text categorization, and spatial sensor placement, highlight the advantages of the proposed approaches.","PeriodicalId":124011,"journal":{"name":"Proceedings of the 23rd international conference on Machine learning","volume":"16 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2006-06-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125905441","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: 352
A new approach to data driven clustering 一种数据驱动聚类的新方法
Proceedings of the 23rd international conference on Machine learning Pub Date : 2006-06-25 DOI: 10.1145/1143844.1143852
Arik Azran, Zoubin Ghahramani
{"title":"A new approach to data driven clustering","authors":"Arik Azran, Zoubin Ghahramani","doi":"10.1145/1143844.1143852","DOIUrl":"https://doi.org/10.1145/1143844.1143852","url":null,"abstract":"We consider the problem of clustering in its most basic form where only a local metric on the data space is given. No parametric statistical model is assumed, and the number of clusters is learned from the data. We introduce, analyze and demonstrate a novel approach to clustering where data points are viewed as nodes of a graph, and pairwise similarities are used to derive a transition probability matrix P for a Markov random walk between them. The algorithm automatically reveals structure at increasing scales by varying the number of steps taken by this random walk. Points are represented as rows of Pt, which are the t-step distributions of the walk starting at that point; these distributions are then clustered using a KL-minimizing iterative algorithm. Both the number of clusters, and the number of steps that 'best reveal' it, are found by optimizing spectral properties of P.","PeriodicalId":124011,"journal":{"name":"Proceedings of the 23rd international conference on Machine learning","volume":"4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2006-06-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128685603","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: 23
PAC model-free reinforcement learning PAC无模型强化学习
Proceedings of the 23rd international conference on Machine learning Pub Date : 2006-06-25 DOI: 10.1145/1143844.1143955
Alexander L. Strehl, Lihong Li, Eric Wiewiora, J. Langford, M. Littman
{"title":"PAC model-free reinforcement learning","authors":"Alexander L. Strehl, Lihong Li, Eric Wiewiora, J. Langford, M. Littman","doi":"10.1145/1143844.1143955","DOIUrl":"https://doi.org/10.1145/1143844.1143955","url":null,"abstract":"For a Markov Decision Process with finite state (size S) and action spaces (size A per state), we propose a new algorithm---Delayed Q-Learning. We prove it is PAC, achieving near optimal performance except for Õ(SA) timesteps using O(SA) space, improving on the Õ(S2 A) bounds of best previous algorithms. This result proves efficient reinforcement learning is possible without learning a model of the MDP from experience. Learning takes place from a single continuous thread of experience---no resets nor parallel sampling is used. Beyond its smaller storage and experience requirements, Delayed Q-learning's per-experience computation cost is much less than that of previous PAC algorithms.","PeriodicalId":124011,"journal":{"name":"Proceedings of the 23rd international conference on Machine learning","volume":"28 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2006-06-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132151988","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: 479
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