2012 11th International Conference on Machine Learning and Applications最新文献_第8页

Prognosis Based on Handling Drifts in Dynamical Environments: Application to a Wind Turbine Benchmark 动态环境中基于漂移处理的预测:在风力发电机基准测试中的应用

2012 11th International Conference on Machine Learning and Applications Pub Date : 2012-12-12 DOI: 10.1109/ICMLA.2012.131

Antoine Chammas, E. Duviella, S. Lecoeuche

引用次数: 6

An Efficient Algorithm for top-k Queries on Uncertain Data Streams 不确定数据流上top-k查询的一种高效算法

2012 11th International Conference on Machine Learning and Applications Pub Date : 2012-12-12 DOI: 10.1109/ICMLA.2012.57

Caiyan Dai, Ling Chen, Yixin Chen, Keming Tang

引用次数: 1

Multi-atlas Based Image Selection with Label Image Constraint 基于标签图像约束的多图集图像选择

2012 11th International Conference on Machine Learning and Applications Pub Date : 2012-12-12 DOI: 10.1109/ICMLA.2012.232

Yihui Cao, Xuelong Li, Pingkun Yan

引用次数: 8

Augmented Coupled Dictionary Learning for Image Super-Resolution 图像超分辨率增强耦合字典学习

2012 11th International Conference on Machine Learning and Applications Pub Date : 2012-12-12 DOI: 10.1109/ICMLA.2012.52

M. Rushdi, J. Ho

{"title":"Augmented Coupled Dictionary Learning for Image Super-Resolution","authors":"M. Rushdi, J. Ho","doi":"10.1109/ICMLA.2012.52","DOIUrl":"https://doi.org/10.1109/ICMLA.2012.52","url":null,"abstract":"Recent approaches in image super-resolution suggest learning dictionary pairs to model the relationship between low-resolution and high-resolution image patches with sparsity constraints on the patch representation. Most of the previous approaches in this direction assume for simplicity that the sparse codes for a low-resolution patch are equal to those of the corresponding high-resolution patch. However, this invariance assumption is not quite accurate especially for large scaling factors where the optimal weights and indices of representative features are not fixed across the scaling transformation. In this paper, we propose an augmented coupled dictionary learning scheme that compensates for the inaccuracy of the invariance assumption. First, we learn a dictionary for the low-resolution image space. Then, we compute an augmented dictionary in the high-resolution image space where novel augmented dictionary atoms are inferred from the training error of the low-resolution dictionary. For a low-resolution test image, the sparse codes of the low-resolution patches and the lowresolution dictionary training error are combined with the trained high-resolution dictionary to produce a high-resolution image. Our experimental results compare favourably with the non-augmented scheme.","PeriodicalId":157399,"journal":{"name":"2012 11th International Conference on Machine Learning and Applications","volume":"201 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-12-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123017726","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: 9

Integrating Machine Learning Into a Medical Decision Support System to Address the Problem of Missing Patient Data 将机器学习集成到医疗决策支持系统中，以解决丢失患者数据的问题

2012 11th International Conference on Machine Learning and Applications Pub Date : 2012-12-12 DOI: 10.1109/ICMLA.2012.82

Atif Khan, J. Doucette, R. Cohen, D. Lizotte

引用次数: 19

Comparing Two New Gene Selection Ensemble Approaches with the Commonly-Used Approach 两种新的基因选择集成方法与常用方法的比较

2012 11th International Conference on Machine Learning and Applications Pub Date : 2012-12-12 DOI: 10.1109/ICMLA.2012.175

D. Dittman, T. Khoshgoftaar, Randall Wald, Amri Napolitano

{"title":"Comparing Two New Gene Selection Ensemble Approaches with the Commonly-Used Approach","authors":"D. Dittman, T. Khoshgoftaar, Randall Wald, Amri Napolitano","doi":"10.1109/ICMLA.2012.175","DOIUrl":"https://doi.org/10.1109/ICMLA.2012.175","url":null,"abstract":"Ensemble feature selection has recently become a topic of interest for researchers, especially in the area of bioinformatics. The benefits of ensemble feature selection include increased feature (gene) subset stability and usefulness as well as comparable (or better) classification performance compared to using a single feature selection method. However, existing work on ensemble feature selection has concentrated on data diversity (using a single feature selection method on multiple datasets or sampled data from a single dataset), neglecting two other potential sources of diversity. We present these two new approaches for gene selection, functional diversity (using multiple feature selection technique on a single dataset) and hybrid (a combination of data and functional diversity). To demonstrate the value of these new approaches, we measure the similarity between the feature subsets created by each of the three approaches across twenty-six datasets and ten feature selection techniques (or an ensemble of these techniques as appropriate). We also compare the classification performance of models built using each of the three ensembles. Our results show that the similarity between the functional diversity and hybrid approaches is much higher than the similarity between either of those and data diversity, with the distinction between data diversity and our new approaches being particularly strong for hard-to-learn datasets. In addition to having the highest similarity, functional and hybrid diversity generally show greater classification performance than data diversity, especially when selecting small feature subsets. These results demonstrate that these new approaches can both provide a different feature subset than the existing approach and that the resulting novel feature subset is potentially of interest to researchers. To our knowledge there has been no study which explores these new approaches to ensemble feature selection within the domain of bioinformatics.","PeriodicalId":157399,"journal":{"name":"2012 11th International Conference on Machine Learning and Applications","volume":"81 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-12-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129193549","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

Estimating Software Effort Using an ANN Model Based on Use Case Points 使用基于用例点的人工神经网络模型估算软件工作量

2012 11th International Conference on Machine Learning and Applications Pub Date : 2012-12-12 DOI: 10.1109/ICMLA.2012.138

A. B. Nassif, Luiz Fernando Capretz, D. Ho

引用次数: 48

Adaptive Selection of Helper-Objectives with Reinforcement Learning 基于强化学习的辅助目标自适应选择

2012 11th International Conference on Machine Learning and Applications Pub Date : 2012-12-12 DOI: 10.1109/ICMLA.2012.159

Arina Buzdalova, M. Buzdalov

引用次数: 8

O-linked Glycosylation Site Prediction Using Ensemble of Graphical Models 使用图形模型集合预测o链糖基化位点

2012 11th International Conference on Machine Learning and Applications Pub Date : 2012-12-12 DOI: 10.1109/ICMLA.2012.210

A. Sriram, Feng Luo

引用次数: 0

Combining Parameter Space Search and Meta-learning for Data-Dependent Computational Agent Recommendation 基于参数空间搜索和元学习的数据依赖计算智能体推荐

2012 11th International Conference on Machine Learning and Applications Pub Date : 2012-12-12 DOI: 10.1109/ICMLA.2012.137

O. Kazík, K. Pesková, M. Pilát, Roman Neruda

引用次数: 6