Proceedings of the twenty-first international conference on Machine learning最新文献_第2页

Bias and variance in value function estimation 值函数估计中的偏差和方差

Proceedings of the twenty-first international conference on Machine learning Pub Date : 2004-07-04 DOI: 10.1145/1015330.1015402

Shie Mannor, D. Simester, Peng Sun, J. Tsitsiklis

引用次数: 77

A needle in a haystack: local one-class optimization 大海捞针:局部单类优化

Proceedings of the twenty-first international conference on Machine learning Pub Date : 2004-07-04 DOI: 10.1145/1015330.1015399

K. Crammer, Gal Chechik

引用次数: 64

Learning and discovery of predictive state representations in dynamical systems with reset 具有重置的动态系统中预测状态表示的学习与发现

Proceedings of the twenty-first international conference on Machine learning Pub Date : 2004-07-04 DOI: 10.1145/1015330.1015359

Michael R. James, Satinder Singh

{"title":"Learning and discovery of predictive state representations in dynamical systems with reset","authors":"Michael R. James, Satinder Singh","doi":"10.1145/1015330.1015359","DOIUrl":"https://doi.org/10.1145/1015330.1015359","url":null,"abstract":"Predictive state representations (PSRs) are a recently proposed way of modeling controlled dynamical systems. PSR-based models use predictions of observable outcomes of tests that could be done on the system as their state representation, and have model parameters that define how the predictive state representation changes over time as actions are taken and observations noted. Learning PSR-based models requires solving two subproblems: 1) discovery of the tests whose predictions constitute state, and 2) learning the model parameters that define the dynamics. So far, there have been no results available on the discovery subproblem while for the learning subproblem an approximate-gradient algorithm has been proposed (Singh et al., 2003) with mixed results (it works on some domains and not on others). In this paper, we provide the first discovery algorithm and a new learning algorithm for linear PSRs for the special class of controlled dynamical systems that have a reset operation. We provide experimental verification of our algorithms. Finally, we also distinguish our work from prior work by Jaeger (2000) on observable operator models (OOMs).","PeriodicalId":159792,"journal":{"name":"Proceedings of the twenty-first international conference on Machine learning","volume":"37 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":"125753535","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: 89

Approximate inference by Markov chains on union spaces 联合空间上马尔可夫链的近似推理

Proceedings of the twenty-first international conference on Machine learning Pub Date : 2004-07-04 DOI: 10.1145/1015330.1015396

M. Welling, M. Rosen-Zvi, Y. Teh

引用次数: 0

Learning Bayesian network classifiers by maximizing conditional likelihood 通过最大化条件似然学习贝叶斯网络分类器

Proceedings of the twenty-first international conference on Machine learning Pub Date : 2004-07-04 DOI: 10.1145/1015330.1015339

D. Grossman, Pedro M. Domingos

引用次数: 334

Kernel-based discriminative learning algorithms for labeling sequences, trees, and graphs 用于标记序列、树和图的基于核的判别学习算法

Proceedings of the twenty-first international conference on Machine learning Pub Date : 2004-07-04 DOI: 10.1145/1015330.1015383

H. Kashima, Yuta Tsuboi

引用次数: 27

Active learning using pre-clustering 使用预聚类的主动学习

Proceedings of the twenty-first international conference on Machine learning Pub Date : 2004-07-04 DOI: 10.1145/1015330.1015349

H. Nguyen, A. Smeulders

引用次数: 677

A comparative study on methods for reducing myopia of hill-climbing search in multirelational learning 多关系学习中减少爬坡搜索近视的方法比较研究

Proceedings of the twenty-first international conference on Machine learning Pub Date : 2004-07-04 DOI: 10.1145/1015330.1015334

Lourdes Peña Castillo, S. Wrobel

{"title":"A comparative study on methods for reducing myopia of hill-climbing search in multirelational learning","authors":"Lourdes Peña Castillo, S. Wrobel","doi":"10.1145/1015330.1015334","DOIUrl":"https://doi.org/10.1145/1015330.1015334","url":null,"abstract":"Hill-climbing search is the most commonly used search algorithm in ILP systems because it permits the generation of theories in short running times. However, a well known drawback of this greedy search strategy is its myopia. Macro-operators (or macros for short), a recently proposed technique to reduce the search space explored by exhaustive search, can also be argued to reduce the myopia of hill-climbing search by automatically performing a variable-depth look-ahead in the search space. Surprisingly, macros have not been employed in a greedy learner. In this paper, we integrate macros into a hill-climbing learner. In a detailed comparative study in several domains, we show that indeed a hill-climbing learner using macros performs significantly better than current state-of-the-art systems involving other techniques for reducing myopia, such as fixed-depth look-ahead, template-based look-ahead, beam-search, or determinate literals. In addition, macros, in contrast to some of the other approaches, can be computed fully automatically and do not require user involvement nor special domain properties such as determinacy.","PeriodicalId":159792,"journal":{"name":"Proceedings of the twenty-first international conference on Machine learning","volume":"95 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":"115539743","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: 15

Sequential information bottleneck for finite data 有限数据的顺序信息瓶颈

Proceedings of the twenty-first international conference on Machine learning Pub Date : 2004-07-04 DOI: 10.1145/1015330.1015375

J. Peltonen, J. Sinkkonen, Samuel Kaski

引用次数: 16

Active learning of label ranking functions 主动学习标签排序函数

Proceedings of the twenty-first international conference on Machine learning Pub Date : 2004-07-04 DOI: 10.1145/1015330.1015331

K. Brinker

引用次数: 49