发布求助
文献互助 智能选刊 最新文献

2008 Seventh International Conference on Machine Learning and Applications最新文献

筛选
英文 中文
Text, Image and Vector Graphics Based Appraisal of Contemporary Documents 基于文本、图像和矢量图形的当代文献评价
2008 Seventh International Conference on Machine Learning and Applications Pub Date : 2008-12-11 DOI: 10.1109/ICMLA.2008.39
Sang-chul Lee, W. McFadden, P. Bajcsy
{"title":"Text, Image and Vector Graphics Based Appraisal of Contemporary Documents","authors":"Sang-chul Lee, W. McFadden, P. Bajcsy","doi":"10.1109/ICMLA.2008.39","DOIUrl":"https://doi.org/10.1109/ICMLA.2008.39","url":null,"abstract":"We have designed a framework for content based appraisal of documents. Our motivation is to provide computer assisted support for answering several appraisal criteria according to the general appraisal guidelines in the National Archives and Record Administration (NARA) 1441 directive. The appraisal criteria led us to investigations related to (a) finding groups of PDF documents with similar content, (b) ranking documents according to their creation/ modification time and digital volume, and (c) detecting inconsistency between ranking and content within a group of related documents. The novelty of our work is in designing a methodology and a mathematical framework for document appraisals, and prototyping the framework working with text, image and vector graphics components of PDF documents. We present example results of grouping, ranking and integrity verification for groups of scientific documents about medical topics.","PeriodicalId":381692,"journal":{"name":"2008 Seventh International Conference on Machine Learning and Applications","volume":"60 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2008-12-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115644149","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
Detection of Sequential Outliers Using a Variable Length Markov Model 用变长度马尔可夫模型检测序列异常值
2008 Seventh International Conference on Machine Learning and Applications Pub Date : 2008-12-11 DOI: 10.1109/ICMLA.2008.137
Cécile Low-Kam, Anne Laurent, M. Teisseire
{"title":"Detection of Sequential Outliers Using a Variable Length Markov Model","authors":"Cécile Low-Kam, Anne Laurent, M. Teisseire","doi":"10.1109/ICMLA.2008.137","DOIUrl":"https://doi.org/10.1109/ICMLA.2008.137","url":null,"abstract":"The problem of mining for outliers in sequential datasets is crucial to forward appropriate analysis of data. Therefore, many approaches for the discovery of such anomalies have been proposed. However, most of them use a sample of known typical sequences to build the model. Besides, they remain greedy in terms of memory usage. In this paper we propose an extension of one such approach, based on a probabilistic suffix tree and on a measure of similarity. We add a pruning criterion which reduces the size of the tree while improving the model, and a sharp inequality for the concentration of the measure of similarity, to better sort the outliers. We prove the feasibility of our approach through a set of experiments over a protein database.","PeriodicalId":381692,"journal":{"name":"2008 Seventh International Conference on Machine Learning and Applications","volume":"20 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2008-12-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125542535","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
A User Modeling Approach to Web Based Adaptive Educational Hypermedia Systems 基于Web的自适应教育超媒体系统的用户建模方法
2008 Seventh International Conference on Machine Learning and Applications Pub Date : 2008-12-11 DOI: 10.1109/ICMLA.2008.66
I. Colak, Ş. Sağiroğlu, H. Kahraman
{"title":"A User Modeling Approach to Web Based Adaptive Educational Hypermedia Systems","authors":"I. Colak, Ş. Sağiroğlu, H. Kahraman","doi":"10.1109/ICMLA.2008.66","DOIUrl":"https://doi.org/10.1109/ICMLA.2008.66","url":null,"abstract":"In this paper, a new user modeling approach has been developed to determine the knowledge status of user in Adaptive Educational Hypermedia System (AEHS). The approach is based on forming the domain model and determining relations among elements of that model to decide the knowledge status of user from domain model independently. The proposed method provides flexibility to individual learning or studying steps. In comparison to the literature, the proposed approach enables quick and powerful adaptation for matching instructional needs of users. The difficulties faced in developing such a system are purifying the gathered data, obtaining and evaluating useful data and providing a powerful adaptation effect in a short time.","PeriodicalId":381692,"journal":{"name":"2008 Seventh International Conference on Machine Learning and Applications","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2008-12-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114436889","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
Ensemble Machine Methods for DNA Binding DNA结合的集成机器方法
2008 Seventh International Conference on Machine Learning and Applications Pub Date : 2008-12-11 DOI: 10.1109/ICMLA.2008.114
Yue Fan, M. Kon, C. DeLisi
{"title":"Ensemble Machine Methods for DNA Binding","authors":"Yue Fan, M. Kon, C. DeLisi","doi":"10.1109/ICMLA.2008.114","DOIUrl":"https://doi.org/10.1109/ICMLA.2008.114","url":null,"abstract":"We introduce three ensemble machine learning methods for analysis of biological DNA binding by transcription factors (TFs). The goal is to identify both TF target genes and their binding motifs. Subspace-valued weak learners (formed from an ensemble of different motif finding algorithms) combine candidate motifs as probability weight matrices (PWM), which are then translated into subspaces of a DNA k-mer (string) feature space. Assessing and then integrating highly informative subspaces by machine methods gives more reliable target classification and motif prediction. We compare these target identification methods with probability weight matrix (PWM) rescanning and use of support vector machines on the full k-mer space of the yeast S. cerevisiae. This method, SVMotif-PWM, can significantly improve accuracy in computational identification of TF targets. The software is publicly available at http://cagt10.bu.edu/SVMotif .","PeriodicalId":381692,"journal":{"name":"2008 Seventh International Conference on Machine Learning and Applications","volume":"39 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2008-12-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130413313","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
Comprehensible Models for Predicting Molecular Interaction with Heart-Regulating Genes 预测与心脏调节基因分子相互作用的可理解模型
2008 Seventh International Conference on Machine Learning and Applications Pub Date : 2008-12-11 DOI: 10.1109/ICMLA.2008.130
Cecilia Sönströd, U. Johansson, U. Norinder, Henrik Boström
{"title":"Comprehensible Models for Predicting Molecular Interaction with Heart-Regulating Genes","authors":"Cecilia Sönströd, U. Johansson, U. Norinder, Henrik Boström","doi":"10.1109/ICMLA.2008.130","DOIUrl":"https://doi.org/10.1109/ICMLA.2008.130","url":null,"abstract":"When using machine learning for in silico modeling, the goal is normally to obtain highly accurate predictive models. Often, however, models should also bring insights into interesting relationships in the domain. It is then desirable that machine learning techniques have the ability to obtain small and transparent models, where the user can control the tradeoff between accuracy, comprehensibility and coverage. In this study, three different decision list algorithms are evaluated on a dataset concerning the interaction of molecules with a human gene that regulates heart functioning (hERG). The results show that decision list algorithms can obtain predictive performance not far from the state-of-the-art method random forests, but also that algorithms focusing on accuracy alone may produce complex decision lists that are very hard to interpret. The experiments also show that by sacrificing accuracy only to a limited degree, comprehensibility (measured as both model size and classification complexity) can be improved remarkably.","PeriodicalId":381692,"journal":{"name":"2008 Seventh International Conference on Machine Learning and Applications","volume":"3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2008-12-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127025730","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
Incremental Hybrid Approach for Microarray Classification 微阵列分类的增量混合方法
2008 Seventh International Conference on Machine Learning and Applications Pub Date : 2008-12-11 DOI: 10.1109/ICMLA.2008.134
M. Wani
{"title":"Incremental Hybrid Approach for Microarray Classification","authors":"M. Wani","doi":"10.1109/ICMLA.2008.134","DOIUrl":"https://doi.org/10.1109/ICMLA.2008.134","url":null,"abstract":"The work presented in this paper describes an incremental hybrid approach that employs principal component analysis (PCA) and multiple discriminant analysis (MDA) methods for microarray classification. The paper first describes a hybrid approach that incorporates PCA and Fisher linear discriminant analysis (FDA) for microarray classification. This hybrid approach effectively solves the singular scatter matrix problem caused by small training samples. To increase the effective dimension of the projected subspace the use of MDA instead of FDA is explored. To improve the performance of the system the data is projected to several subspaces incrementally. The resulting incremental hybrid system improves the accuracy of classification. The paper discusses a comprehensive evaluation of the hybrid systems. The hybrid systems were tested on a dataset of 62 samples (40 colon tumor and 22 normal colon tissue). The results show that the use of incremental hybrid system increased the accuracy of classification of microarray data which will lead to better diagnosis of cancer and other diseases.","PeriodicalId":381692,"journal":{"name":"2008 Seventh International Conference on Machine Learning and Applications","volume":"48 7 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2008-12-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121194224","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: 21
Calibrating Random Forests 校准随机森林
2008 Seventh International Conference on Machine Learning and Applications Pub Date : 2008-12-11 DOI: 10.1109/ICMLA.2008.107
Henrik Boström
{"title":"Calibrating Random Forests","authors":"Henrik Boström","doi":"10.1109/ICMLA.2008.107","DOIUrl":"https://doi.org/10.1109/ICMLA.2008.107","url":null,"abstract":"When using the output of classifiers to calculate the expected utility of different alternatives in decision situations, the correctness of predicted class probabilities may be of crucial importance. However, even very accurate classifiers may output class probabilities of rather poor quality. One way of overcoming this problem is by means of calibration, i.e., mapping the original class probabilities to more accurate ones. Previous studies have however indicated that random forests are difficult to calibrate by standard calibration methods. In this work, a novel calibration method is introduced, which is based on a recent finding that probabilities predicted by forests of classification trees have a lower squared error compared to those predicted by forests of probability estimation trees (PETs). The novel calibration method is compared to the two standard methods, Platt scaling and isotonic regression, on 34 datasets from the UCI repository. The experiment shows that random forests of PETs calibrated by the novel method significantly outperform uncalibrated random forests of both PETs and classification trees, as well as random forests calibrated with the two standard methods, with respect to the squared error of predicted class probabilities.","PeriodicalId":381692,"journal":{"name":"2008 Seventh International Conference on Machine Learning and Applications","volume":"76 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2008-12-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116298201","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: 53
Using Neural Networks to Predict Breathing Motion 利用神经网络预测呼吸运动
2008 Seventh International Conference on Machine Learning and Applications Pub Date : 2008-12-11 DOI: 10.1109/ICMLA.2008.136
M. Murphy
{"title":"Using Neural Networks to Predict Breathing Motion","authors":"M. Murphy","doi":"10.1109/ICMLA.2008.136","DOIUrl":"https://doi.org/10.1109/ICMLA.2008.136","url":null,"abstract":"This paper describes the use of neural networks for real-time spatial and temporal tracking of radiotherapy treatment sites during free breathing. These techniques will allow the reduction and possible elimination of dose-limiting motion margins in external-beam radiation delivery plans.","PeriodicalId":381692,"journal":{"name":"2008 Seventh International Conference on Machine Learning and Applications","volume":"18 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2008-12-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134598260","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: 11
A New Neural Network to Process Missing Data without Imputation 一种新的无补全缺失数据处理神经网络
2008 Seventh International Conference on Machine Learning and Applications Pub Date : 2008-12-11 DOI: 10.1109/ICMLA.2008.89
M. Randolph-Gips
{"title":"A New Neural Network to Process Missing Data without Imputation","authors":"M. Randolph-Gips","doi":"10.1109/ICMLA.2008.89","DOIUrl":"https://doi.org/10.1109/ICMLA.2008.89","url":null,"abstract":"This paper introduces the cosine neural network (COSNN) and shows how it can be used to process data with missing components without imputation. It uses a cosine basis function with a weighted norm which can be trained to match the input data, or it can be set to zero to 'ignore' missing data components. The COSNN is compared to feedforward neural networks using deletion and imputation. The COSNN is shown to be superior in both a function approximation and a classification test set.","PeriodicalId":381692,"journal":{"name":"2008 Seventh International Conference on Machine Learning and Applications","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2008-12-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133059419","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: 7
Adaptive Control of Antilock Braking System Using Grey Multilayer Feedforward Neural Networks 基于灰色多层前馈神经网络的防抱死制动系统自适应控制
2008 Seventh International Conference on Machine Learning and Applications Pub Date : 2008-12-11 DOI: 10.1109/ICMLA.2008.15
E. Kayacan, Y. Oniz, O. Kaynak, A. Topalov
{"title":"Adaptive Control of Antilock Braking System Using Grey Multilayer Feedforward Neural Networks","authors":"E. Kayacan, Y. Oniz, O. Kaynak, A. Topalov","doi":"10.1109/ICMLA.2008.15","DOIUrl":"https://doi.org/10.1109/ICMLA.2008.15","url":null,"abstract":"In this paper, a grey neuro-adaptive control algorithm is suggested for Antilock Braking Systems (ABS). The concept of grey system theory, which has a certain prediction capability, offers an alternative approach to conventional control methods. A multilayer neural network and a grey predictor, GM(1,1) model, are combined in the approach proposed in the paper. The grey neural network controller is examined under several different operating conditions and it is shown that the proposed control algorithm anticipates the upcoming values of wheel slip and optimal wheel slip, and takes the necessary action to keep the wheel slip at the desired value. The simulation results indicate that the proposed controller has the ability to control the nonlinear system accurately with little oscillations and with no steady-state error.","PeriodicalId":381692,"journal":{"name":"2008 Seventh International Conference on Machine Learning and Applications","volume":"98 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2008-12-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114507194","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: 1
下一页 尾页
0
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整，为了账户安全，请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
请完成安全验证×
相关产品
×
本文献相关产品
联系我们：info@booksci.cn Book学术提供免费学术资源搜索服务，方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号：604180095
Book学术官方微信
小红书