2011 10th International Conference on Machine Learning and Applications and Workshops最新文献

Mood Classfication from Musical Audio Using User Group-Dependent Models 使用用户组相关模型对音乐音频进行情绪分类

2011 10th International Conference on Machine Learning and Applications and Workshops Pub Date : 2011-12-18 DOI: 10.1109/ICMLA.2011.96

Kyogu Lee, Minsuk Cho

引用次数: 6

Building an Ensemble of Probabilistic Classifiers for Lung Nodule Interpretation 构建肺结节解释的概率分类器集合

2011 10th International Conference on Machine Learning and Applications and Workshops Pub Date : 2011-12-18 DOI: 10.1109/ICMLA.2011.44

D. Zinovev, J. Furst, D. Raicu

{"title":"Building an Ensemble of Probabilistic Classifiers for Lung Nodule Interpretation","authors":"D. Zinovev, J. Furst, D. Raicu","doi":"10.1109/ICMLA.2011.44","DOIUrl":"https://doi.org/10.1109/ICMLA.2011.44","url":null,"abstract":"When examining Computed Tomography (CT) scans of lungs for potential abnormalities, radiologists make use of lung nodule's semantic characteristics during the analysis. Computer-Aided Diagnostic Characterization (CADc) systems can act as an aid - predicting ratings of these semantic characteristics to aid radiologists in evaluating the nodule and potentially improve the quality and consistency of diagnosis. In our work, we propose a system for predicting the distribution of radiologists' opinions using a probabilistic multi-class classification approach based on combination of belief decision trees and ADABoost ensemble learning approach. To train and test our system we use the National Cancer Institute (NCI) Lung Image Database Consortium (LIDC) dataset, which includes semantic annotations by up to four radiologists for each one of the 914 nodules. Furthermore, we evaluate our probabilistic multi-class classifications using a novel distance-threshold curve technique intended for assessing the performance of uncertain classification systems. We conclude that for the majority of semantic characteristics there exists a set of parameters that significantly improves the performance of the ensemble over the single classifier.","PeriodicalId":439926,"journal":{"name":"2011 10th International Conference on Machine Learning and Applications and Workshops","volume":"4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-12-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117113950","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: 15

Mobile Robot Self-Localization Based on Omnidirectional Vision and Gaussian Models 基于全向视觉和高斯模型的移动机器人自定位

2011 10th International Conference on Machine Learning and Applications and Workshops Pub Date : 2011-12-18 DOI: 10.1109/ICMLA.2011.95

Diego Campos-Sobrino, Francisco Coral-Sabido, Martha Varguez-Moo, Víctor Uc Cetina, A. Espinosa-Romero

引用次数: 0

Learning to Rank Using Markov Random Fields 使用马尔可夫随机场学习排序

2011 10th International Conference on Machine Learning and Applications and Workshops Pub Date : 2011-12-18 DOI: 10.1109/ICMLA.2011.157

Antonino Freno, Tiziano Papini, Michelangelo Diligenti

{"title":"Learning to Rank Using Markov Random Fields","authors":"Antonino Freno, Tiziano Papini, Michelangelo Diligenti","doi":"10.1109/ICMLA.2011.157","DOIUrl":"https://doi.org/10.1109/ICMLA.2011.157","url":null,"abstract":"Learning to rank from examples is an important task in modern Information Retrieval systems like Web search engines, where the large number of available features makes hard to manually devise high-performing ranking functions. This paper presents a novel approach to learning-to-rank, which can natively integrate any target metric with no modifications. The target metric is optimized via maximum-likelihood estimation of a probability distribution over the ranks, which are assumed to follow a Boltzmann distribution. Unlike other approaches in the literature like BoltzRank, this approach does not rely on maximizing the expected value of the target score as a proxy of the optimization of target metric. This has both theoretical and performance advantages as the expected value can not be computed both accurately and efficiently. Furthermore, our model employs the pseudo-likelihood as an accurate surrogate of the likelihood to avoid to explicitly compute the normalization factor of the Boltzmann distribution, which is intractable in this context. The experimental results show that the approach provides state-of-the-art results on various benchmarks and on a dataset built from the logs of a commercial search engine.","PeriodicalId":439926,"journal":{"name":"2011 10th International Conference on Machine Learning and Applications and Workshops","volume":"117 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-12-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122330806","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: 4

Large Margin Classifier Based on Hyperdisks 基于hyperdisk的大间距分类器

2011 10th International Conference on Machine Learning and Applications and Workshops Pub Date : 2011-12-18 DOI: 10.1109/ICMLA.2011.86

Hakan Cevikalp

引用次数: 3

Introducing Flow Field Forecasting 流场预测简介

2011 10th International Conference on Machine Learning and Applications and Workshops Pub Date : 2011-12-18 DOI: 10.1109/ICMLA.2011.82

Michael Frey, Kyle A. Caudle

引用次数: 5

Charge Prediction of Lipid Fragments in Mass Spectrometry 质谱法中脂质碎片电荷预测

2011 10th International Conference on Machine Learning and Applications and Workshops Pub Date : 2011-12-18 DOI: 10.1109/ICMLA.2011.45

B. Schrom, L. Kangas, Bojana Ginovska-Pangovska, T. Metz, John H. Miller

引用次数: 1

Infinite Decision Agent Ensemble Learning System for Credit Risk Analysis 信用风险分析的无限决策代理集成学习系统

2011 10th International Conference on Machine Learning and Applications and Workshops Pub Date : 2011-12-18 DOI: 10.1109/ICMLA.2011.80

Shukai Li, I. Tsang, N. Chaudhari

引用次数: 0

L0-Regularized Parametric Non-negative Factorization for Analyzing Composite Signals l0 -正则化参数非负分解分析复合信号

2011 10th International Conference on Machine Learning and Applications and Workshops Pub Date : 2011-12-18 DOI: 10.1109/ICMLA.2011.84

Takumi Kobayashi, Kenji Watanabe, N. Otsu

{"title":"L0-Regularized Parametric Non-negative Factorization for Analyzing Composite Signals","authors":"Takumi Kobayashi, Kenji Watanabe, N. Otsu","doi":"10.1109/ICMLA.2011.84","DOIUrl":"https://doi.org/10.1109/ICMLA.2011.84","url":null,"abstract":"Signal sequences are practically observed as composites in which a few number of factor signals are linearly combined with non-negative weights. Based on prior physical knowledge about the target, the factors can be modeled as parametric functions, and their parameter values benefit further analyses. In this paper, we present a novel factorization method for the composite signals in terms of parametric factor functions. The method optimizes both the factor weights and the parameter values in the factor functions. While the parameter values are simply optimized by gradient descent, we propose L0-regularized non-negative least squares (L0-NNLS) for optimizing the factor weights. In L0-NNLS, both L0 regularization and non-negativity constraint are imposed on the weights in the least squares to enhance the sparsity as much as possible. Since so regularized least squares is NPhard, we propose a stepwise forward/backward optimization to efficiently solve it in an approximated manner. Due to the sparsity by the L0-NNLS, the proposed factorization method can automatically discover the inherent number of factor functions as well as the parametric functions themselves by estimating their parameter values. In the experiments on factorization of simulated signals and practical biological signals, the proposed method exhibits favorable performances.","PeriodicalId":439926,"journal":{"name":"2011 10th International Conference on Machine Learning and Applications and Workshops","volume":"4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-12-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130593115","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

Nonlinear RANSAC Optimization for Parameter Estimation with Applications to Phagocyte Transmigration 非线性RANSAC优化参数估计及其在吞噬细胞迁移中的应用

2011 10th International Conference on Machine Learning and Applications and Workshops Pub Date : 2011-12-18 DOI: 10.1109/ICMLA.2011.104

Mingon Kang, Jean X. Gao, Liping Tang

{"title":"Nonlinear RANSAC Optimization for Parameter Estimation with Applications to Phagocyte Transmigration","authors":"Mingon Kang, Jean X. Gao, Liping Tang","doi":"10.1109/ICMLA.2011.104","DOIUrl":"https://doi.org/10.1109/ICMLA.2011.104","url":null,"abstract":"Developing vigorous mathematical models and estimating accurate parameters within feasible computational time are two indispensable parts to build reliable system models for representing biological properties of the system and for producing reliable simulation. For a complex biological system with limited observations, one of the daunting tasks is the large number of unknown parameters in the mathematical modeling whose values directly determine the performance of computational modeling. To tackle this problem, we have developed a data-driven global optimization method, nonlinear RANSAC, based on Random Sample Consensus (a.k.a. RANSAC) method, for parameter estimation of nonlinear system models. Conventional RANSAC method is sound and simple, but it is oriented for linear system models. We not only adopt the strengths of RANSAC, but also extend the method for nonlinear systems with outstanding performance. As a specific application example, we have targeted understanding phagocyte transmigration which is involved in the fibrosis process for biomedical device implantation. With well-defined mathematical nonlinear equations of the system, nonlinear RANSAC is performed for the parameter estimation. Moreover, simulations of the system for propagation prediction over the time are conducted under both normal conditions and knock-out conditions. In order to evaluate the general performance of the method, we also applied the method to signalling pathways where mathematical equations which are representing interaction of proteins are generated using ordinary differential equations as a general format, and public data sets for nonlinear regression evaluation are used to assess its performance.","PeriodicalId":439926,"journal":{"name":"2011 10th International Conference on Machine Learning and Applications and Workshops","volume":"25 2","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-12-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114114388","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: 8