Proceedings of the twenty-first international conference on Machine learning最新文献_第3页

Tractable learning of large Bayes net structures from sparse data 基于稀疏数据的大型贝叶斯网络结构的可处理学习

Proceedings of the twenty-first international conference on Machine learning Pub Date : 2004-07-04 DOI: 10.1145/1015330.1015406

A. Goldenberg, A. Moore

引用次数: 70

Boosting margin based distance functions for clustering 增强基于边际的聚类距离函数

Proceedings of the twenty-first international conference on Machine learning Pub Date : 2004-07-04 DOI: 10.1145/1015330.1015389

T. Hertz, Aharon Bar-Hillel, D. Weinshall

{"title":"Boosting margin based distance functions for clustering","authors":"T. Hertz, Aharon Bar-Hillel, D. Weinshall","doi":"10.1145/1015330.1015389","DOIUrl":"https://doi.org/10.1145/1015330.1015389","url":null,"abstract":"The performance of graph based clustering methods critically depends on the quality of the distance function used to compute similarities between pairs of neighboring nodes. In this paper we learn distance functions by training binary classifiers with margins. The classifiers are defined over the product space of pairs of points and are trained to distinguish whether two points come from the same class or not. The signed margin is used as the distance value. Our main contribution is a distance learning method (DistBoost), which combines boosting hypotheses over the product space with a weak learner based on partitioning the original feature space. Each weak hypothesis is a Gaussian mixture model computed using a semi-supervised constrained EM algorithm, which is trained using both unlabeled and labeled data. We also consider SVM and decision trees boosting as margin based classifiers in the product space. We experimentally compare the margin based distance functions with other existing metric learning methods, and with existing techniques for the direct incorporation of constraints into various clustering algorithms. Clustering performance is measured on some benchmark databases from the UCI repository, a sample from the MNIST database, and a data set of color images of animals. In most cases the DistBoost algorithm significantly and robustly outperformed its competitors.","PeriodicalId":159792,"journal":{"name":"Proceedings of the twenty-first international conference on Machine learning","volume":"2002 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":"127331546","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: 128

Ensembles of nested dichotomies for multi-class problems 多类问题的嵌套二分类集成

Proceedings of the twenty-first international conference on Machine learning Pub Date : 2004-07-04 DOI: 10.1145/1015330.1015363

E. Frank, Stefan Kramer

{"title":"Ensembles of nested dichotomies for multi-class problems","authors":"E. Frank, Stefan Kramer","doi":"10.1145/1015330.1015363","DOIUrl":"https://doi.org/10.1145/1015330.1015363","url":null,"abstract":"Nested dichotomies are a standard statistical technique for tackling certain polytomous classification problems with logistic regression. They can be represented as binary trees that recursively split a multi-class classification task into a system of dichotomies and provide a statistically sound way of applying two-class learning algorithms to multi-class problems (assuming these algorithms generate class probability estimates). However, there are usually many candidate trees for a given problem and in the standard approach the choice of a particular tree is based on domain knowledge that may not be available in practice. An alternative is to treat every system of nested dichotomies as equally likely and to form an ensemble classifier based on this assumption. We show that this approach produces more accurate classifications than applying C4.5 and logistic regression directly to multi-class problems. Our results also show that ensembles of nested dichotomies produce more accurate classifiers than pairwise classification if both techniques are used with C4.5, and comparable results for logistic regression. Compared to error-correcting output codes, they are preferable if logistic regression is used, and comparable in the case of C4.5. An additional benefit is that they generate class probability estimates. Consequently they appear to be a good general-purpose method for applying binary classifiers to multi-class problems.","PeriodicalId":159792,"journal":{"name":"Proceedings of the twenty-first international conference on Machine learning","volume":"117 4","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2004-07-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132477692","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: 133

Co-EM support vector learning 协同电磁支持向量学习

Proceedings of the twenty-first international conference on Machine learning Pub Date : 2004-07-04 DOI: 10.1145/1015330.1015350

Ulf Brefeld, T. Scheffer

引用次数: 210

A maximum entropy approach to species distribution modeling 物种分布建模的最大熵方法

Proceedings of the twenty-first international conference on Machine learning Pub Date : 2004-07-04 DOI: 10.1145/1015330.1015412

Steven J. Phillips, Miroslav Dudík, R. Schapire

引用次数: 2066

Diverse ensembles for active learning 多样化的合奏，积极学习

Proceedings of the twenty-first international conference on Machine learning Pub Date : 2004-07-04 DOI: 10.1145/1015330.1015385

Prem Melville, R. Mooney

引用次数: 371

Dynamic abstraction in reinforcement learning via clustering 聚类强化学习中的动态抽象

Proceedings of the twenty-first international conference on Machine learning Pub Date : 2004-07-04 DOI: 10.1145/1015330.1015355

Shie Mannor, Ishai Menache, Amit Hoze, Uri Klein

引用次数: 238

Learning to fly by combining reinforcement learning with behavioural cloning 通过结合强化学习和行为克隆来学习飞行

Proceedings of the twenty-first international conference on Machine learning Pub Date : 2004-07-04 DOI: 10.1145/1015330.1015384

E. Morales, C. Sammut

引用次数: 45

Apprenticeship learning via inverse reinforcement learning 通过逆强化学习的学徒学习

Proceedings of the twenty-first international conference on Machine learning Pub Date : 2004-07-04 DOI: 10.1145/1015330.1015430

P. Abbeel, A. Ng

引用次数: 3045

Learning first-order rules from data with multiple parts: applications on mining chemical compound data 从多部分数据中学习一阶规则:在化合物数据挖掘中的应用

Proceedings of the twenty-first international conference on Machine learning Pub Date : 2004-07-04 DOI: 10.1145/1015330.1015447

C. Nattee, S. Sinthupinyo, M. Numao, T. Okada

{"title":"Learning first-order rules from data with multiple parts: applications on mining chemical compound data","authors":"C. Nattee, S. Sinthupinyo, M. Numao, T. Okada","doi":"10.1145/1015330.1015447","DOIUrl":"https://doi.org/10.1145/1015330.1015447","url":null,"abstract":"Inductive learning of first-order theory based on examples has serious bottleneck in the enormous hypothesis search space needed, making existing learning approaches perform poorly when compared to the propositional approach. Moreover, in order to choose the appropiate candidates, all Inductive Logic Programming (ILP) systems only use quantitive information, e.g. number of examples covered and length of rules, which is insufficient for search space having many similar candidates. This paper introduces a novel approach to improve ILP by incorporating the qualitative information into the search heuristics by focusing only on a kind of data where one instance consists of several parts, as well as relations among parts. This approach aims to find the hypothesis describing each class by using both individual and relational characteristics of parts of examples. This kind of data can be found in various domains, especially in representing chemical compound structure. Each compound is composed of atoms as parts, and bonds as relations between two atoms. We apply the proposed approach for discovering rules describing the activity of compounds from their structures from two real-world datasets: mutagenicity in nitroaromatic compounds and dopamine antagonist compounds. The results were compared to the existing method using ten-fold cross validation, and we found that the proposed method significantly produced more accurate results in prediction.","PeriodicalId":159792,"journal":{"name":"Proceedings of the twenty-first international conference on Machine learning","volume":"295 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":"127775549","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