2006 5th International Conference on Machine Learning and Applications (ICMLA'06)最新文献_第5页

Web Robot Learning Powered by Bluetooth Communication System 基于蓝牙通信系统的网络机器人学习

2006 5th International Conference on Machine Learning and Applications (ICMLA'06) Pub Date : 2006-12-14 DOI: 10.1109/ICMLA.2006.53

Ş. Sağiroğlu, N. Yilmaz, M. Wani

引用次数: 11

Robust Model Selection Using Cross Validation: A Simple Iterative Technique for Developing Robust Gene Signatures in Biomedical Genomics Applications 使用交叉验证的稳健模型选择:在生物医学基因组学应用中开发稳健基因签名的简单迭代技术

2006 5th International Conference on Machine Learning and Applications (ICMLA'06) Pub Date : 2006-12-14 DOI: 10.1109/ICMLA.2006.45

R. Venkatesh, C. Rowland, Hongjin Huang, Olivia T. Abar, J. Sninsky

{"title":"Robust Model Selection Using Cross Validation: A Simple Iterative Technique for Developing Robust Gene Signatures in Biomedical Genomics Applications","authors":"R. Venkatesh, C. Rowland, Hongjin Huang, Olivia T. Abar, J. Sninsky","doi":"10.1109/ICMLA.2006.45","DOIUrl":"https://doi.org/10.1109/ICMLA.2006.45","url":null,"abstract":"The iterative technique proposed in this paper provides an effective way to select a robust model in wide data settings such as in genomics and gene expression studies where number of markers Gt number of samples. This technique can be quite useful when an independent test set is not available and crossvalidation is used as a validation step. It removes many of the ambiguities surrounding the final model selection process giving a computationally simple and transparent way to choose a robust model. The robust model selection is mainly accomplished by utilizing the fold frequencies of markers selected in repeated crossvalidation experiments in a direct and effective manner. The technique, both in terms of feature selection and classification is not method specific and therefore can be used with different sets of feature selection and classification methods. The usefulness of this technique extends even to situations where independent test set is available. Using this technique it allows one to squeeze extra performance out of the feature selection procedure and increase the odds of replication in an independent test set. Frequently only one test set is available and in this case use of this technique can help avoid repeated use of the test set. Availability of techniques such as one described in this study can be of great practical value in developing biomedical genomic applications e.g., molecular diagnostic tests. The technique was successfully applied to a complex real world data set and significant improvements were demonstrated in terms of compactness, accuracy and generalizability of the model","PeriodicalId":297071,"journal":{"name":"2006 5th International Conference on Machine Learning and Applications (ICMLA'06)","volume":"33 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2006-12-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115081229","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: 6

Impact of the Predicted Protein Structural Content on Prediction of Structural Classes for the Twilight Zone Proteins 预测蛋白质结构含量对模糊区蛋白质结构分类预测的影响

2006 5th International Conference on Machine Learning and Applications (ICMLA'06) Pub Date : 2006-12-01 DOI: 10.1109/ICMLA.2006.27

Lukasz Kurgan, M. Rahbari, L. Homaeian

{"title":"Impact of the Predicted Protein Structural Content on Prediction of Structural Classes for the Twilight Zone Proteins","authors":"Lukasz Kurgan, M. Rahbari, L. Homaeian","doi":"10.1109/ICMLA.2006.27","DOIUrl":"https://doi.org/10.1109/ICMLA.2006.27","url":null,"abstract":"This paper addresses in silico prediction of protein structural classes as defined in the SCOP database. The SCOP defines total of 11 classes, while majority of proteins are classified to the 4 classes: all-alpha all-beta alpha/beta, and alpha+beta. The main goals of this paper are to experimentally evaluate the impact of predicted protein secondary structure content on the structural class prediction and to develop a novel protein sequence representation. The experiments include application of three protein sequence representations and four classifiers to prediction of both 4 and 11 structural classes. The predictions are performed using a large dataset of low homology (twilight zone) sequences. The proposed sequence representation includes the predicted structural content, which provides the strongest contribution towards classification, composition and composition moment vectors, hydrophobic autocorrelations, chemical group composition and molecular weight of the protein. The predicted content values are shown on average to improve the prediction accuracy by 3.3% and 4.2% for the 4 and 11 classes, respectively, when compared to sequence representation that does not utilize this information. Finally, we propose a very compact, 20 dimensional sequence representation that is shown to improve the prediction accuracy by 5.1-8.5% when compared with recently published results","PeriodicalId":297071,"journal":{"name":"2006 5th International Conference on Machine Learning and Applications (ICMLA'06)","volume":"25 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2006-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124915399","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

Explaining Winning Poker--A Data Mining Approach 解释赢得扑克——一种数据挖掘方法

2006 5th International Conference on Machine Learning and Applications (ICMLA'06) Pub Date : 2006-12-01 DOI: 10.1109/ICMLA.2006.23

U. Johansson, Cecilia Sönströd, L. Niklasson

引用次数: 10

Identity Representability of Facial Expressions: An Evaluation Using Feature Pixel Distributions 面部表情的身份可表征性:使用特征像素分布的评估

2006 5th International Conference on Machine Learning and Applications (ICMLA'06) Pub Date : 2006-12-01 DOI: 10.1109/ICMLA.2006.26

Qi Li, C. Kambhamettu

引用次数: 1