2010 Ninth International Conference on Machine Learning and Applications最新文献_第5页

Heuristic Method for Discriminative Structure Learning of Markov Logic Networks 马尔可夫逻辑网络判别结构学习的启发式方法

2010 Ninth International Conference on Machine Learning and Applications Pub Date : 2010-12-12 DOI: 10.1109/ICMLA.2010.31

Quang-Thang Dinh, M. Exbrayat, Christel Vrain

引用次数: 2

Pairwise Constrained Clustering with Group Similarity-Based Patterns 基于组相似度模式的成对约束聚类

2010 Ninth International Conference on Machine Learning and Applications Pub Date : 2010-12-12 DOI: 10.1109/ICMLA.2010.45

Tianming Hu, Chuanren Liu, Jing Sun, S. Sung, P. Ng

引用次数: 0

Evolutionary Selection of Regressional Predictors to Enhance the Performance of Microfossil-Based Paleotemperture Proxies 回归预测因子的进化选择以提高微化石古温度代理的性能

2010 Ninth International Conference on Machine Learning and Applications Pub Date : 2010-12-12 DOI: 10.1109/ICMLA.2010.63

A. Assareh, L. Volkert, J. Ortiz

{"title":"Evolutionary Selection of Regressional Predictors to Enhance the Performance of Microfossil-Based Paleotemperture Proxies","authors":"A. Assareh, L. Volkert, J. Ortiz","doi":"10.1109/ICMLA.2010.63","DOIUrl":"https://doi.org/10.1109/ICMLA.2010.63","url":null,"abstract":"Using microfossil-based transfer functions, domain scientists from the field of pale oceanography seek to reconstruct environmental conditions at various times in the past. This is accomplished by first determining a quantitative relationship between a forcing function, such as temperature, and the modern for aminiferal response using a calibration data set based on environmental data from an oceanographic atlas and faunas generally extracted from sediment core tops. The method can be employed with a variety of environmental variables, but reconstruction of surface temperature is often the objective. The relationship developed using this training or calibration data set is then applied to down core data to infer past environmental conditions. The statistical methods that have been previously applied in this area can be grouped into three categories: linear regression based approaches, locally weighted regressions and neural networks. In addition to introducing some other learning algorithms including regression trees, bagging trees, random forest and support vector regression to this domain, in this study we suggest the use of model combination approaches to enhance the precision of estimation. By initializing with a pool of diverse predictors using a variety of learning algorithms and different samplings from the training and attribute set, a genetic algorithm was applied to select the best team of predictors. The optimal team was dominated by artificial neural network predictors suggesting their superiority over other methods tested with this type of data. The results also show the efficacy of the proposed approach compared to the other models.","PeriodicalId":336514,"journal":{"name":"2010 Ninth International Conference on Machine Learning and Applications","volume":"39 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":"117092696","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: 0

Neuropathic Pain Scale Based Clustering for Subgroup Analysis in Pain Medicine 基于聚类的神经性疼痛量表在疼痛医学中的亚组分析

2010 Ninth International Conference on Machine Learning and Applications Pub Date : 2010-12-12 DOI: 10.1109/ICMLA.2010.51

Guangzhi Qu, Hui Wu, I. Sethi, C. Hartrick

引用次数: 0

Interestingness Detection in Sports Audio Broadcasts 体育音频广播中的趣味性检测

2010 Ninth International Conference on Machine Learning and Applications Pub Date : 2010-12-12 DOI: 10.1109/ICMLA.2010.99

Sam Davies, Denise Bland

引用次数: 4

Learning Bayesian Networks for Improved Instruction Cache Analysis 学习贝叶斯网络改进指令缓存分析

2010 Ninth International Conference on Machine Learning and Applications Pub Date : 2010-12-12 DOI: 10.1109/ICMLA.2010.68

M. Bartlett, I. Bate, J. Cussens

引用次数: 5

Self-Optimizing a Clustering-based Tag Recommender for Social Bookmarking Systems 社会化书签系统中基于聚类的标签推荐自优化

2010 Ninth International Conference on Machine Learning and Applications Pub Date : 2010-12-12 DOI: 10.1109/ICMLA.2010.93

Malik Tahir Hassan, Asim Karim, F. Javed, N. Arshad

引用次数: 5

Heterogeneous Imitation Learning from Demonstrators of Varying Physiology and Skill 来自不同生理和技能示范的异质模仿学习

2010 Ninth International Conference on Machine Learning and Applications Pub Date : 2010-12-12 DOI: 10.1109/ICMLA.2010.23

Jeff Allen, J. Anderson

{"title":"Heterogeneous Imitation Learning from Demonstrators of Varying Physiology and Skill","authors":"Jeff Allen, J. Anderson","doi":"10.1109/ICMLA.2010.23","DOIUrl":"https://doi.org/10.1109/ICMLA.2010.23","url":null,"abstract":"Imitation learning enables a learner to improve its abilities by observing others. Most robotic imitation learning systems only learn from demonstrators that are homogeneous physiologically (i.e. the same size and mode of locomotion) and in terms of skill level. To successfully learn from physically heterogeneous robots that may also vary in ability, the imitator must be able to abstract behaviours it observes and approximate them with its own actions, which may be very different than those of the demonstrator. This paper describes an approach to imitation learning from heterogeneous demonstrators, using global vision for observations. It supports learning from physiologically different demonstrators (wheeled and legged, of various sizes), and self-adapts to demonstrators with varying levels of skill. The latter allows a bias toward demonstrators that are successful in the domain, but also allows different parts of a task to be learned from different individuals (that is, worthwhile parts of a task can still be learned from a poorly-performing demonstrator). We assume the imitator has no initial knowledge of the observable effects of its own actions, and train a set of Hidden Markov Models to map observations to actions and create an understanding of the imitator's own abilities. We then use a combination of tracking sequences of primitives and predicting future primitives from existing combinations using forward models to learn abstract behaviours from the demonstrations of others. This approach is evaluated using a group of heterogeneous robots that have been previously used in RoboCup soccer competitions.","PeriodicalId":336514,"journal":{"name":"2010 Ninth International Conference on Machine Learning and Applications","volume":"18 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":"128376935","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: 0

A Hybrid Multi-classifier to Characterize and Interpret Hemiparetic Patients Gait Coordination 混合多分类器表征和解释偏瘫患者的步态协调

2010 Ninth International Conference on Machine Learning and Applications Pub Date : 2010-12-12 DOI: 10.1109/ICMLA.2010.90

Laurent Hartert, M. S. Mouchaweh

{"title":"A Hybrid Multi-classifier to Characterize and Interpret Hemiparetic Patients Gait Coordination","authors":"Laurent Hartert, M. S. Mouchaweh","doi":"10.1109/ICMLA.2010.90","DOIUrl":"https://doi.org/10.1109/ICMLA.2010.90","url":null,"abstract":"The characterization of inter-segmental coordination patterns in hemi paretic gait is interesting to improve the management of hemiparetic patients. Indeed, the analysis of the coordination patterns can help clinician to establish patient diagnosis and to choose a treatment. The coordination patterns used in this article were obtained from the Continuous Relative Phase (CRP) measure in the sagittal plane. The CRP correlates angle positions and velocity of two segments, i.e. parts of the patient leg, over each phase of the gait cycle. Thigh-shank and shank-foot CRPs were measured for 66 hemiparetic patients, 27 healthy subjects and 14 patients pre and post treatment. CRPs signals are classified using a multi-classifier. This classification permits to discriminate gait patterns for hemiparetic and healthy subjects. The multi-classifier is based on a structural and a statistical approaches used in parallel. The structural part of the proposed hybrid method keeps links between the data issued from CRPs and the statistical part converts CRPs into spatial scalar parameters. Then, using a similarity measure this approach permits to quantify the global gait coordination improvement of patients after a therapeutic treatment. The proposed approach uses only interpretable parameters in order to let the classification results be physically understandable.","PeriodicalId":336514,"journal":{"name":"2010 Ninth International Conference on Machine Learning and Applications","volume":"62 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":"123122894","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: 1

Incremental kNN Classifier Exploiting Correct-Error Teacher for Activity Recognition 利用纠错教师进行活动识别的增量kNN分类器

2010 Ninth International Conference on Machine Learning and Applications Pub Date : 2010-12-12 DOI: 10.1109/ICMLA.2010.72

Kilian Förster, Samuel Monteleone, Alberto Calatroni, D. Roggen, G. Tröster

{"title":"Incremental kNN Classifier Exploiting Correct-Error Teacher for Activity Recognition","authors":"Kilian Förster, Samuel Monteleone, Alberto Calatroni, D. Roggen, G. Tröster","doi":"10.1109/ICMLA.2010.72","DOIUrl":"https://doi.org/10.1109/ICMLA.2010.72","url":null,"abstract":"Non-stationary data distributions are a challenge in activity recognition from body worn motion sensors. Classifier models have to be adapted online to maintain a high recognition performance. Typical approaches for online learning are either unsupervised and potentially unstable, or require ground truth information which may be expensive to obtain. As an alternative we propose a teacher signal that can be provided by the user in a minimally obtrusive way. It indicates if the predicted activity for a feature vector is correct or wrong. To exploit this information we propose a novel incremental online learning strategy to adapt a k-nearest-neighbor classifier from instances that are indicated to be correctly or wrongly classified. We characterize our approach on an artificial dataset with abrupt distribution change that simulates a new user of an activity recognition system. The adapted classifier reaches the same accuracy as a classifier trained specifically for the new data distribution. The learning based on the provided correct - error signal also results in a faster learning speed compared to online learning from ground truth. We validate our approach on a real world gesture recognition dataset. The adapted classifiers achieve an accuracy of 78.6% compared to the subject independent baseline of 68.3%.","PeriodicalId":336514,"journal":{"name":"2010 Ninth International Conference on Machine Learning and Applications","volume":"64 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":"133980943","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: 44