2010 Ninth International Conference on Machine Learning and Applications最新文献_第9页

Support Vector Machines on GPU with Sparse Matrix Format 稀疏矩阵格式的GPU支持向量机

2010 Ninth International Conference on Machine Learning and Applications Pub Date : 2010-12-12 DOI: 10.1109/ICMLA.2010.53

Tsung-Kai Lin, Shao-Yi Chien

引用次数: 37

Using an Infinite Von Mises-Fisher Mixture Model to Cluster Treatment Beam Directions in External Radiation Therapy 用无限Von Mises-Fisher混合模型聚类体外放射治疗光束方向

2010 Ninth International Conference on Machine Learning and Applications Pub Date : 2010-12-12 DOI: 10.1109/ICMLA.2010.114

M. Bangert, Philipp Hennig, U. Oelfke

{"title":"Using an Infinite Von Mises-Fisher Mixture Model to Cluster Treatment Beam Directions in External Radiation Therapy","authors":"M. Bangert, Philipp Hennig, U. Oelfke","doi":"10.1109/ICMLA.2010.114","DOIUrl":"https://doi.org/10.1109/ICMLA.2010.114","url":null,"abstract":"We present a method for fully automated selection of treatment beam ensembles for external radiation therapy. We reformulate the beam angle selection problem as a clustering problem of locally ideal beam orientations distributed on the unit sphere. For this purpose we construct an infinite mixture of von Mises-Fisher distributions, which is suited in general for density estimation from data on the D-dimensional sphere. Using a nonparametric Dirichlet process prior, our model infers probability distributions over both the number of clusters and their parameter values. We describe an efficient Markov chain Monte Carlo inference algorithm for posterior inference from experimental data in this model. The performance of the suggested beam angle selection framework is illustrated for one intra-cranial, pancreas, and prostate case each. The infinite von Mises-Fisher mixture model (iMFMM) creates between 18 and 32 clusters, depending on the patient anatomy. This suggests to use the iMFMM directly for beam ensemble selection in robotic radio surgery, or to generate low-dimensional input for both subsequent optimization of trajectories for arc therapy and beam ensemble selection for conventional radiation therapy.","PeriodicalId":336514,"journal":{"name":"2010 Ninth International Conference on Machine Learning and Applications","volume":"4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-12-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121090506","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: 45

On-Line Adaptation of Exploration in the One-Armed Bandit with Covariates Problem 单臂土匪协变量问题在线自适应探索

2010 Ninth International Conference on Machine Learning and Applications Pub Date : 2010-12-12 DOI: 10.1109/ICMLA.2010.74

A. Sykulski, N. Adams, N. Jennings

{"title":"On-Line Adaptation of Exploration in the One-Armed Bandit with Covariates Problem","authors":"A. Sykulski, N. Adams, N. Jennings","doi":"10.1109/ICMLA.2010.74","DOIUrl":"https://doi.org/10.1109/ICMLA.2010.74","url":null,"abstract":"Many sequential decision making problems require an agent to balance exploration and exploitation to maximise long-term reward. Existing policies that address this tradeoff typically have parameters that are set a priori to control the amount of exploration. In finite-time problems, the optimal values of these parameters are highly dependent on the problem faced. In this paper, we propose adapting the amount of exploration performed on-line, as information is gathered by the agent. To this end we introduce a novel algorithm, e-ADAPT, which has no free parameters. The algorithm adapts as it plays and sequentially chooses whether to explore or exploit, driven by the amount of uncertainty in the system. We provide simulation results for the one armed bandit with covariates problem, which demonstrate the effectiveness of e-ADAPT to correctly control the amount of exploration in finite-time problems and yield rewards that are close to optimally tuned off-line policies. Furthermore, we show that e-ADAPT is robust to a high-dimensional covariate, as well as misspecified models. Finally, we describe how our methods could be extended to other sequential decision making problems, such as dynamic bandit problems with changing reward structures.","PeriodicalId":336514,"journal":{"name":"2010 Ninth International Conference on Machine Learning and Applications","volume":"5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-12-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127545346","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: 17

Model-Based Co-clustering for Continuous Data 基于模型的连续数据共聚类

2010 Ninth International Conference on Machine Learning and Applications Pub Date : 2010-12-12 DOI: 10.1109/ICMLA.2010.33

M. Nadif, G. Govaert

引用次数: 27

Map-TreeMaps: A New Approach for Hierarchical and Topological Clustering Map-TreeMaps:层次和拓扑聚类的新方法

2010 Ninth International Conference on Machine Learning and Applications Pub Date : 2010-12-12 DOI: 10.1109/ICMLA.2010.136

Hanene Azzag, M. Lebbah, A. Arfaoui

引用次数: 0

Incremental Nyström Low-Rank Decomposition for Dynamic Learning 增量Nyström低秩分解动态学习

2010 Ninth International Conference on Machine Learning and Applications Pub Date : 2010-12-12 DOI: 10.1109/ICMLA.2010.87

Lin Zhang, Hongyu Li

引用次数: 2

From Serve-on-Demand to Serve-on-Need: A Game Theoretic Approach 从按需服务到按需服务:一个博弈论方法

2010 Ninth International Conference on Machine Learning and Applications Pub Date : 2010-12-12 DOI: 10.1109/ICMLA.2010.12

Yong Lin, F. Makedon

引用次数: 0

Pre-image Problem in Manifold Learning and Dimensional Reduction Methods 流形学习与降维方法中的预像问题

2010 Ninth International Conference on Machine Learning and Applications Pub Date : 2010-12-12 DOI: 10.1109/ICMLA.2010.146

Omar Arif, P. Vela, W. Daley

引用次数: 5

Discovering and Characterizing Hidden Variables in Streaming Multivariate Time Series 流多元时间序列中隐变量的发现与表征

2010 Ninth International Conference on Machine Learning and Applications Pub Date : 2010-12-12 DOI: 10.1109/ICMLA.2010.144

Soumi Ray, T. Oates

引用次数: 2

System Identification with Multi-Agent-based Evolutionary Computation Using a Local Optimization Kernel 基于局部优化核的多agent进化计算系统辨识

2010 Ninth International Conference on Machine Learning and Applications Pub Date : 2010-12-12 DOI: 10.1109/ICMLA.2010.130

S. Bohlmann, V. Klinger, H. Szczerbicka

{"title":"System Identification with Multi-Agent-based Evolutionary Computation Using a Local Optimization Kernel","authors":"S. Bohlmann, V. Klinger, H. Szczerbicka","doi":"10.1109/ICMLA.2010.130","DOIUrl":"https://doi.org/10.1109/ICMLA.2010.130","url":null,"abstract":"Most technical and manufacturing processes are based on an empiric process understanding, there only very incomplete formal relations exist. To establish a process model, the identification of the appropriate process is essential. In addition, this process model has to feature a quality of execution to enable forward-looking properties like an online prediction mode. This report argues that the agent-based identification is appropriate to this modelling issue. Although there were many predecessor approaches, which tried to design formal models of manufacturing processes, all of them fell short of the data based identification of complex systems, like paper manufacturing: complex systems consisting of continuous and discrete parts, called hybrid manufacturing systems. This paper focuses on the system identification with agent based evolutionary computation using a local optimization kernel. It presents the system architecture and introduces a data based identification method with different local optimization lgorithms. Finally we consider the characteristics of an identification framework with large-scale data processing. We close with identification results related to the 2-step optimization algorithm.","PeriodicalId":336514,"journal":{"name":"2010 Ninth International Conference on Machine Learning and Applications","volume":"103 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-12-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130351522","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: 11