2010 Ninth International Conference on Machine Learning and Applications最新文献_第7页

Discovering Knowledge Rules with Multi-Objective Evolutionary Computing 基于多目标进化计算的知识规则发现

2010 Ninth International Conference on Machine Learning and Applications Pub Date : 2010-12-12 DOI: 10.1109/ICMLA.2010.25

Rafael Giusti, Gustavo E. A. P. A. Batista

引用次数: 2

Smoothing Gene Expression Using Biological Networks 利用生物网络平滑基因表达

2010 Ninth International Conference on Machine Learning and Applications Pub Date : 2010-12-12 DOI: 10.1109/ICMLA.2010.85

Yue Fan, M. Kon, Shinuk Kim, C. DeLisi

{"title":"Smoothing Gene Expression Using Biological Networks","authors":"Yue Fan, M. Kon, Shinuk Kim, C. DeLisi","doi":"10.1109/ICMLA.2010.85","DOIUrl":"https://doi.org/10.1109/ICMLA.2010.85","url":null,"abstract":"Gene expression (micro array) data have been used widely in bioinformatics. The expression data of a large number of genes from small numbers of subjects are used to identify informative biomarkers that may predict or help in diagnosing some disorders. More recently, increasing amounts of information from underlying relationships of the expressed genes have become available, and workers have started to investigate algorithms which can use such a priori information to improve classification or regression based on gene expression. In this paper, we describe three novel machine learning algorithms for regularizing (smoothing) micro array expression values defined on gene sets with known prior network or metric structures, and which exploit this gene interaction information. These regularized expression values can be used with any machine classifier with the goal of better classification. In this paper, standard smoothing (denoising) techniques previously developed for functions on Euclidean spaces are extended to allow smoothing of micro array expression feature vectors using distance measures defined by biological networks. Such a priori smoothing (denoising) of the feature vectors using metrics on the index space (here the space of genes) yields better signal to noise ratios in the data. When tested on two breast cancer datasets, support vector machine classifiers trained on the smoothed expression values obtain better areas under ROC curves in two cancer datasets.","PeriodicalId":336514,"journal":{"name":"2010 Ninth International Conference on Machine Learning and Applications","volume":"29 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":"125304600","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: 3

Dynamic Batch Size Selection for Batch Mode Active Learning in Biometrics 生物识别中批模式主动学习的动态批大小选择

2010 Ninth International Conference on Machine Learning and Applications Pub Date : 2010-12-12 DOI: 10.1109/ICMLA.2010.10

Shayok Chakraborty, V. Balasubramanian, S. Panchanathan

{"title":"Dynamic Batch Size Selection for Batch Mode Active Learning in Biometrics","authors":"Shayok Chakraborty, V. Balasubramanian, S. Panchanathan","doi":"10.1109/ICMLA.2010.10","DOIUrl":"https://doi.org/10.1109/ICMLA.2010.10","url":null,"abstract":"Robust biometric recognition is of paramount importance in security and surveillance applications. In face based biometric systems, data is usually collected using a video camera with high frame rate and thus the captured data has high redundancy. Selecting the appropriate instances from this data to update a classification model, is a significant, yet valuable challenge. Active learning methods have gained popularity in identifying the salient and exemplar data instances from superfluous sets. Batch mode active learning schemes attempt to select a batch of samples simultaneously rather than updating the model after selecting every single data point. Existing work on batch mode active learning assume a fixed batch size, which is not a practical assumption in biometric recognition applications. In this paper, we propose a novel framework to dynamically select the batch size using clustering based unsupervised learning techniques. We also present a batch mode active learning strategy specially suited to handle the high redundancy in biometric datasets. The results obtained on the challenging VidTIMIT and MOBIO datasets corroborate the superiority of dynamic batch size selection over static batch size and also certify the potential of the proposed active learning scheme in being used for real world biometric recognition applications.","PeriodicalId":336514,"journal":{"name":"2010 Ninth International Conference on Machine Learning and Applications","volume":"52 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":"126589556","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: 5

Multimodal Parameter-exploring Policy Gradients 多模态参数探索策略梯度

2010 Ninth International Conference on Machine Learning and Applications Pub Date : 2010-12-12 DOI: 10.1109/ICMLA.2010.24

Frank Sehnke, Alex Graves, Christian Osendorfer, J. Schmidhuber

引用次数: 15

Validating Meronymy Hypotheses with Support Vector Machines and Graph Kernels 用支持向量机和图核验证同名假设

2010 Ninth International Conference on Machine Learning and Applications Pub Date : 2010-12-12 DOI: 10.1109/ICMLA.2010.43

Tim vor der Brück, H. Helbig

{"title":"Validating Meronymy Hypotheses with Support Vector Machines and Graph Kernels","authors":"Tim vor der Brück, H. Helbig","doi":"10.1109/ICMLA.2010.43","DOIUrl":"https://doi.org/10.1109/ICMLA.2010.43","url":null,"abstract":"There is a substantial body of work on the extraction of relations from texts, most of which is based on pattern matching or on applying tree kernel functions to syntactic structures. Whereas pattern application is usually more efficient, tree kernels can be superior when assessed by the F-measure. In this paper, we introduce a hybrid approach to extracting meronymy relations, which is based on both patterns and kernel functions. In a first step, meronymy relation hypotheses are extracted from a text corpus by applying patterns. In a second step these relation hypotheses are validated by using several shallow features and a graph kernel approach. In contrast to other meronymy extraction and validation methods which are based on surface or syntactic representations we use a purely semantic approach based on semantic networks. This involves analyzing each sentence of the Wikipedia corpus by a deep syntactico-semantic parser and converting it into a semantic network. Meronymy relation hypotheses are extracted from the semantic networks by means of an automated theorem prover, which employs a set of logical axioms and patterns in the form of semantic networks. The meronymy candidates are then validated by means of a graph kernel approach based on common walks. The evaluation shows that this method achieves considerably higher accuracy, recall, and F-measure than a method using purely shallow validation.","PeriodicalId":336514,"journal":{"name":"2010 Ninth International Conference on Machine Learning and Applications","volume":"24 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":"123300367","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

Automatic Segmentation of the Prostate Using a Genetic Algorithm for Prostate Cancer Treatment Planning 基于遗传算法的前列腺自动分割用于前列腺癌治疗规划

2010 Ninth International Conference on Machine Learning and Applications Pub Date : 2010-12-12 DOI: 10.1109/ICMLA.2010.115

Melanie Mitchell, J. Tanyi, A. Hung

引用次数: 4

Predicting End-to-end Network Load 预测端到端网络负载

2010 Ninth International Conference on Machine Learning and Applications Pub Date : 2010-12-12 DOI: 10.1109/ICMLA.2010.145

A. Vashist, S. Mau, A. Poylisher, R. Chadha, Abhrajit Ghosh

引用次数: 3

A Chunking Method for Euclidean Distance Matrix Calculation on Large Dataset Using Multi-GPU 基于多gpu的大数据集欧几里得距离矩阵分块计算方法

2010 Ninth International Conference on Machine Learning and Applications Pub Date : 2010-12-12 DOI: 10.1109/ICMLA.2010.38

Qi Li, V. Kecman, R. Salman

{"title":"A Chunking Method for Euclidean Distance Matrix Calculation on Large Dataset Using Multi-GPU","authors":"Qi Li, V. Kecman, R. Salman","doi":"10.1109/ICMLA.2010.38","DOIUrl":"https://doi.org/10.1109/ICMLA.2010.38","url":null,"abstract":"Calculating Euclidean distance matrix is a data intensive operation and becomes computationally prohibitive for large datasets. Recent development of Graphics Processing Units (GPUs) has produced superb performance on scientific computing problems using massive parallel processing cores. However, due to the limited size of device memory, many GPU based algorithms have low capability in solving problems with large datasets. In this paper, a chunking method is proposed to calculate Euclidean distance matrix on large datasets. This is not only designed for scalability in multi-GPU environment but also to maximize the computational capability of each individual GPU device. We first implement a fast GPU algorithm that is suitable for calculating sub matrices of Euclidean distance matrix. Then we utilize a Map-Reduce like framework to split the final distance matrix calculation into many small independent jobs of calculating partial distance matrices, which can be efficiently solved by our GPU algorithm. The framework also dynamically allocates GPU resources to those independent jobs for maximum performance. The experimental results have shown a speed up of 15x on datasets which contain more than half million data points.","PeriodicalId":336514,"journal":{"name":"2010 Ninth International Conference on Machine Learning and Applications","volume":"95 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":"128925188","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: 69

A Relative Tendency Based Stock Market Prediction System 基于相对趋势的股票市场预测系统

2010 Ninth International Conference on Machine Learning and Applications Pub Date : 2010-12-12 DOI: 10.1109/ICMLA.2010.151

ManChon U, K. Rasheed

引用次数: 2

Constrained Nonnegative Tensor Factorization for Clustering 约束非负张量分解聚类

2010 Ninth International Conference on Machine Learning and Applications Pub Date : 2010-12-12 DOI: 10.1109/ICMLA.2010.152

Wei Peng

引用次数: 1