2012 IEEE International Workshop on Machine Learning for Signal Processing最新文献_第6页

Classifier-based affinities for clustering sets of vectors 向量聚类集的基于分类器的亲和性

2012 IEEE International Workshop on Machine Learning for Signal Processing Pub Date : 2012-11-12 DOI: 10.1109/MLSP.2012.6349760

Dario García-García, Raúl Santos-Rodríguez, E. Parrado-Hernández

引用次数: 1

A novel scheme for diffusion networks with least-squares adaptive combiners 一种具有最小二乘自适应组合器的扩散网络新方案

2012 IEEE International Workshop on Machine Learning for Signal Processing Pub Date : 2012-11-12 DOI: 10.1109/MLSP.2012.6349767

Jesus Fernandez-Bes, L. A. Azpicueta-Ruiz, Magno T. M. Silva, J. Arenas-García

引用次数: 14

Perturbation regulated kernel regressors for supervised machine learning 用于监督机器学习的扰动调节核回归量

2012 IEEE International Workshop on Machine Learning for Signal Processing Pub Date : 2012-11-12 DOI: 10.1109/MLSP.2012.6349743

S. Kung, Pei-Yuan Wu

{"title":"Perturbation regulated kernel regressors for supervised machine learning","authors":"S. Kung, Pei-Yuan Wu","doi":"10.1109/MLSP.2012.6349743","DOIUrl":"https://doi.org/10.1109/MLSP.2012.6349743","url":null,"abstract":"This paper develops a kernel perturbation-regulated (KPR) regressor based on the errors-in-variables models. KPR offers a strong smoothing capability critical to the robustness of regression or classification results. For Gaussian cases, the notion of orthogonal polynomials is instrumental to optimal estimation and its error analysis. More exactly, the regressor may be expressed as a linear combination of many simple Hermite Regressors, each focusing on one (and only one) orthogonal polynomial. For Gaussian or non-Gaussian cases, this paper formally establishes a “Two-Projection Theorem” allowing the estimation task to be divided into two projection stages: the first projection reveals the effect of model-induced error (caused by under-represented regressor models) while the second projection reveals the extra estimation error due to the (inevitable) input measuring error. The two-projection analysis leads to a closed-form error formula critical for order/error tradeoff. The simulation results not only confirm the theoretical prediction but also demonstrate superiority of KPR over the conventional ridge regression method in MSE reduction.","PeriodicalId":262601,"journal":{"name":"2012 IEEE International Workshop on Machine Learning for Signal Processing","volume":"33 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":"126399668","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

The discounted cumulative margin penalty: Rank-learning with a list-wise loss and pair-wise margins 贴现累积保证金惩罚:具有列表明智损失和成对明智保证金的排名学习

2012 IEEE International Workshop on Machine Learning for Signal Processing Pub Date : 2012-11-12 DOI: 10.1109/MLSP.2012.6349807

C. Renjifo, C. Carmen

引用次数: 12

Pseudo inputs for pairwise learning with Gaussian processes 高斯过程成对学习的伪输入

2012 IEEE International Workshop on Machine Learning for Signal Processing Pub Date : 2012-11-12 DOI: 10.1109/MLSP.2012.6349812

J. Nielsen, B. S. Jensen, Jan Larsen

引用次数: 2

Distributed microphone array processing for speech source separation with classifier fusion 基于分类器融合的声源分离分布式麦克风阵列处理

2012 IEEE International Workshop on Machine Learning for Signal Processing Pub Date : 2012-11-12 DOI: 10.1109/MLSP.2012.6349782

M. Souden, K. Kinoshita, Marc Delcroix, T. Nakatani

{"title":"Distributed microphone array processing for speech source separation with classifier fusion","authors":"M. Souden, K. Kinoshita, Marc Delcroix, T. Nakatani","doi":"10.1109/MLSP.2012.6349782","DOIUrl":"https://doi.org/10.1109/MLSP.2012.6349782","url":null,"abstract":"We propose a new approach for clustering and separating competing speech signals using a distributed microphone array (DMA). This approach can be viewed as an extension of expectation-maximization (EM)-based source separation to DMAs. To achieve distributed processing, we assume the conditional independence (with respect to sources' activities) of the normalized recordings of different nodes. By doing so, only the posterior probabilities of sources' activities need to be shared between nodes. Consequently, the EM algorithm is formulated such that at the expectation step, local posterior probabilities are estimated locally and shared between nodes. In the maximization step, every node fuses the received probabilities via either product or sum rules and estimates its local parameters. We show that, even if we make binary decisions (presence/ absence of speech) during EM iterations instead of transmitting continuous posterior probability values, we can achieve separation without causing significant speech distortion. Our preliminary investigations demonstrate that the proposed processing technique approaches the centralized solution and can outperform Oracle best node-wise clustering in terms of objective source separation metrics.","PeriodicalId":262601,"journal":{"name":"2012 IEEE International Workshop on Machine Learning for Signal Processing","volume":"138 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":"124329581","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: 16

Kernel-based parametric validity index for assessing clusters from microarray gene expression data 基于核函数的微阵列基因表达数据聚类评估参数效度指标

2012 IEEE International Workshop on Machine Learning for Signal Processing Pub Date : 2012-11-12 DOI: 10.1109/MLSP.2012.6349780

Rui Fa, A. Nandi

引用次数: 1

Estimation of the forgetting factor in kernel recursive least squares 核递归最小二乘中遗忘因子的估计

2012 IEEE International Workshop on Machine Learning for Signal Processing Pub Date : 2012-11-12 DOI: 10.1109/MLSP.2012.6349749

S. V. Vaerenbergh, I. Santamaría, M. Lázaro-Gredilla

引用次数: 30

Modelling dense relational data 密集关系数据建模

2012 IEEE International Workshop on Machine Learning for Signal Processing Pub Date : 2012-11-12 DOI: 10.1109/MLSP.2012.6349747

Tue Herlau, Morten Mørup, Mikkel N. Schmidt, L. K. Hansen

引用次数: 11

ECG-based biometrics: A real time classification approach 基于脑电图的生物识别:一种实时分类方法

2012 IEEE International Workshop on Machine Learning for Signal Processing Pub Date : 2012-11-12 DOI: 10.1109/MLSP.2012.6349735

A. Lourenço, H. Silva, A. Fred

引用次数: 46