2012 IEEE International Workshop on Machine Learning for Signal Processing最新文献_第7页

Latent Dirichlet learning for hierarchical segmentation 层次分割的潜在狄利克雷学习

2012 IEEE International Workshop on Machine Learning for Signal Processing Pub Date : 2012-11-12 DOI: 10.1109/MLSP.2012.6349772

Jen-Tzung Chien, C. Chueh

引用次数: 0

A subspace learning algorithm for microwave scattering signal classification with application to wood quality assessment 微波散射信号分类的子空间学习算法及其在木材质量评价中的应用

2012 IEEE International Workshop on Machine Learning for Signal Processing Pub Date : 2012-11-12 DOI: 10.1109/MLSP.2012.6349728

Yinan Yu, T. McKelvey

引用次数: 4

The eighth annual MLSP competition: Second place team 第八届MLSP年度大赛:第二名

2012 IEEE International Workshop on Machine Learning for Signal Processing Pub Date : 2012-11-12 DOI: 10.1109/MLSP.2012.6349770

H. Huttunen, Timo Erkkilä, P. Ruusuvuori, T. Manninen

引用次数: 1

Comprehensive analysis of multiple microarray datasets by binarization of consensus partition matrix 基于共识划分矩阵二值化的多微阵列数据集综合分析

2012 IEEE International Workshop on Machine Learning for Signal Processing Pub Date : 2012-11-12 DOI: 10.1109/MLSP.2012.6349787

Basel Abu-Jamous, Rui Fa, D. Roberts, A. Nandi

{"title":"Comprehensive analysis of multiple microarray datasets by binarization of consensus partition matrix","authors":"Basel Abu-Jamous, Rui Fa, D. Roberts, A. Nandi","doi":"10.1109/MLSP.2012.6349787","DOIUrl":"https://doi.org/10.1109/MLSP.2012.6349787","url":null,"abstract":"Clustering methods have been increasingly applied over gene expression datasets. Different results are obtained when different clustering methods are applied over the same dataset as well as when the same set of genes is clustered in different microarray datasets. Most approaches cluster genes' profiles from only one dataset, either by a single method or an ensemble of methods; we propose using the binarization of consensus partition matrix (Bi-CoPaM) method to analyze comprehensively the results of clustering the same set of genes by different clustering methods and from different datasets. A tunable consensus result is generated and can be tightened or widened to control the assignment of the doubtful genes that have been assigned to different clusters in different individual results. We apply this over a subset of 384 yeast genes by using four clustering methods and five microarray datasets. The results demonstrate the power of Bi-CoPaM in fusing many different individual results in a tunable consensus result and that such comprehensive analysis can overcome many of the defects in any of the individual datasets or clustering methods.","PeriodicalId":262601,"journal":{"name":"2012 IEEE International Workshop on Machine Learning for Signal Processing","volume":"118 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-11-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134106908","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: 2

Language informed bandwidth expansion 基于语言的带宽扩展

2012 IEEE International Workshop on Machine Learning for Signal Processing Pub Date : 2012-11-12 DOI: 10.1109/MLSP.2012.6349783

Jinyu Han, G. Mysore, Bryan Pardo

引用次数: 10

Joint feature and model training for minimum detection errors applied to speech subword detection 联合特征和模型训练最小检测误差应用于语音子词检测

2012 IEEE International Workshop on Machine Learning for Signal Processing Pub Date : 2012-11-12 DOI: 10.1109/MLSP.2012.6349729

M. H. Johnsen, Alfonso M. Canterla

引用次数: 2

Facial expression recognition with robust covariance estimation and Support Vector Machines 基于鲁棒协方差估计和支持向量机的面部表情识别

2012 IEEE International Workshop on Machine Learning for Signal Processing Pub Date : 2012-11-12 DOI: 10.1109/MLSP.2012.6349762

N. Vretos, A. Tefas, I. Pitas

引用次数: 1

Handling missing features in maximum margin Bayesian network classifiers 处理最大边际贝叶斯网络分类器中的缺失特征

2012 IEEE International Workshop on Machine Learning for Signal Processing Pub Date : 2012-11-12 DOI: 10.1109/MLSP.2012.6349804

Sebastian Tschiatschek, N. Mutsam, F. Pernkopf

{"title":"Handling missing features in maximum margin Bayesian network classifiers","authors":"Sebastian Tschiatschek, N. Mutsam, F. Pernkopf","doi":"10.1109/MLSP.2012.6349804","DOIUrl":"https://doi.org/10.1109/MLSP.2012.6349804","url":null,"abstract":"The Comprehensive Nuclear-Test-Ban Treaty Organization (CTBTO) records hydroacoustic data to detect nuclear explosions1. This enables verification of the Comprehensive Nuclear-Test-Ban Treaty once it has entered into force. The detection can be considered as a classification problem discriminating noise-like, earthquake-caused and explosion-like data. Classification of the recorded data is challenging because it suffers from large amounts of missing features. While the classification performance of support vector machines has been evaluated, no such results for Bayesian network classifiers are available. We provide these results using classifiers with generatively and discriminatively optimized parameters and employing different imputation methods. In case of discriminatively optimized parameters, Bayesian network classifiers slightly outperform support vector machines. For optimizing the parameters discriminatively, we extend the formulation of maximum margin Bayesian network classifiers to missing features and latent variables. The advantage of these classifiers over classifiers with generatively optimized parameters is demonstrated in experiments.","PeriodicalId":262601,"journal":{"name":"2012 IEEE International Workshop on Machine Learning for Signal Processing","volume":"27 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-11-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115278499","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: 2

Context Dependent Spectral Unmixing 上下文相关的光谱分解

2012 IEEE International Workshop on Machine Learning for Signal Processing Pub Date : 2012-11-12 DOI: 10.1109/MLSP.2012.6349750

Hamdi Jenzri, H. Frigui, P. Gader

引用次数: 3

Online learning with kernels: Overcoming the growing sum problem 基于核函数的在线学习:克服增长和问题

2012 IEEE International Workshop on Machine Learning for Signal Processing Pub Date : 2012-11-12 DOI: 10.1109/MLSP.2012.6349811

Abhishek Singh, N. Ahuja, P. Moulin

引用次数: 47