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Control of a re-entrant line manufacturing model with a reinforcement learning approach 用强化学习方法控制再入生产线制造模型
Sixth International Conference on Machine Learning and Applications (ICMLA 2007) Pub Date : 2007-12-13 DOI: 10.1109/ICMLA.2007.78
J. Ramírez-Hernández, E. Fernandez
{"title":"Control of a re-entrant line manufacturing model with a reinforcement learning approach","authors":"J. Ramírez-Hernández, E. Fernandez","doi":"10.1109/ICMLA.2007.78","DOIUrl":"https://doi.org/10.1109/ICMLA.2007.78","url":null,"abstract":"This paper presents the application of a reinforcement learning (RL) approach for the near-optimal control of a re-entrant line manufacturing (RLM) model. The RL approach utilizes an algorithm based on a gradient-descent TD(lambda) method to obtain both estimates of the optimal cost function and the control actions. Numerical experiments demonstrated the efficacy of the approach in estimating optimal actions by showing close approximations in performance w.r.t. the optimal strategy. Generalizations of the RL approach may have the advantage of scaling appropriately for RLM models with different dimensions in the state and action spaces.","PeriodicalId":448863,"journal":{"name":"Sixth International Conference on Machine Learning and Applications (ICMLA 2007)","volume":"27 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2007-12-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125285086","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: 31
A Statistical Algorithm to Discover Knowledge in Medical Data Sources 医学数据源中知识发现的统计算法
Sixth International Conference on Machine Learning and Applications (ICMLA 2007) Pub Date : 2007-12-13 DOI: 10.1109/ICMLA.2007.91
Alexander Senf, C. Leonard, J. DeLeo
{"title":"A Statistical Algorithm to Discover Knowledge in Medical Data Sources","authors":"Alexander Senf, C. Leonard, J. DeLeo","doi":"10.1109/ICMLA.2007.91","DOIUrl":"https://doi.org/10.1109/ICMLA.2007.91","url":null,"abstract":"Developing intelligent tools to extract information from data collections has long been of critical importance in fields such as knowledge discovery, information retrieval, pattern recognition, and databases. With the advent of electronic medical records and medical data repositories there is new potential to apply these techniques to the analysis of biomedical data sets. Looking for complex patterns within large biomedical data repositories and discovering previously unexpected associations can be of particular interest for understanding the physiology and functionality of the human body as well as tracing the roots of diseases. In the context of a research hospital these analyses may lead to further directed research, better diagnostic capabilities, and improved patient outcomes. This paper describes an implementation of a knowledge discovery algorithm aimed at such data sets.","PeriodicalId":448863,"journal":{"name":"Sixth International Conference on Machine Learning and Applications (ICMLA 2007)","volume":"15 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2007-12-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114311659","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
SVMotif: A Machine Learning Motif Algorithm SVMotif:一个机器学习Motif算法
Sixth International Conference on Machine Learning and Applications (ICMLA 2007) Pub Date : 2007-12-13 DOI: 10.1109/ICMLA.2007.105
M. Kon, Yue Fan, Dustin T. Holloway, C. DeLisi
{"title":"SVMotif: A Machine Learning Motif Algorithm","authors":"M. Kon, Yue Fan, Dustin T. Holloway, C. DeLisi","doi":"10.1109/ICMLA.2007.105","DOIUrl":"https://doi.org/10.1109/ICMLA.2007.105","url":null,"abstract":"We describe SVMotif, a support vector machine-based learning algorithm for identification of cellular DNA transcription factor (TF) motifs extrapolated from known TF-gene interactions. An important aspect of this procedure is its ability to utilize negative target information (examples of likely non-targets) as well as positive information. Applications involve situations where clusters of genes are distinguished in experiments with known transcription factors without known binding locations. We apply this to yeast TF data with target identifications from ChlP-chip and other sources, and compare performance with Gibbs sampling methods such as BioProspector. We verify that in yeast this method implies well-defined and cross-validated statistical correlations between TF binding and secondary motifs whose binding properties (either with the primary TF or other possible promoters) are not certain, and discuss some implications of this. SVMotif can be a useful standalone method or a complement to existing techniques, and it will be made publicly available.","PeriodicalId":448863,"journal":{"name":"Sixth International Conference on Machine Learning and Applications (ICMLA 2007)","volume":"13 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2007-12-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114235067","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
Normalized Linear Transform for Cross-Platform Microarray Data Integration 跨平台微阵列数据集成的归一化线性变换
Sixth International Conference on Machine Learning and Applications (ICMLA 2007) Pub Date : 2007-12-13 DOI: 10.1109/ICMLA.2007.65
Huilin Xiong, Ya Zhang, Xue-wen Chen
{"title":"Normalized Linear Transform for Cross-Platform Microarray Data Integration","authors":"Huilin Xiong, Ya Zhang, Xue-wen Chen","doi":"10.1109/ICMLA.2007.65","DOIUrl":"https://doi.org/10.1109/ICMLA.2007.65","url":null,"abstract":"With microarray data being dramatically accumulated, integrating data from related studies represents a natural way to increase sample size so that more reliable statistical analysis may be performed. However, inherent variation among different microarray platforms makes the data integration not a trivial task. In this paper, we present a simple and effective integration scheme, called normalized linear transform (NLT), to combine data from different microarray platforms. The NLT scheme is compared with three other integration schemes for two tasks: classification analysis and gene marker selection. Our experiments demonstrate that the NLT scheme performs best in terms of classification accuracy under various classification settings, and leads to more biologically significant marker genes.","PeriodicalId":448863,"journal":{"name":"Sixth International Conference on Machine Learning and Applications (ICMLA 2007)","volume":"105 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2007-12-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129282581","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
Logistic Ensembles for Random Spherical Linear Oracles 随机球形线性预言机的逻辑集成
Sixth International Conference on Machine Learning and Applications (ICMLA 2007) Pub Date : 2007-12-13 DOI: 10.1109/ICMLA.2007.104
Leif E. Peterson, M. Coleman
{"title":"Logistic Ensembles for Random Spherical Linear Oracles","authors":"Leif E. Peterson, M. Coleman","doi":"10.1109/ICMLA.2007.104","DOIUrl":"https://doi.org/10.1109/ICMLA.2007.104","url":null,"abstract":"A random spherical linear oracle (RSLO) ensemble classifier for DNA microarray gene expression data is proposed. The oracle assigns different training(testing) samples to 2 sub- classifiers of the same type using hyperplane splits in order to increase the diversity of voting results since errors are not shared across sub-classifiers. Eleven classifiers were evaluated for performance as the base classifier including k nearest neighbor (kNN), naive Bayes classifier (NBC), linear discriminant analysis (LDA), learning vector quantization (LVQ1), polytomous logistic regression (PLOG), artificial neural networks (ANN), constricted particle swarm optimization (CPSO), kernel regression (KREG), radial basis function networks (RBFN), gradient descent support vector machines (SVMGD), and least squares support vector machines (SVMLS). Logistic ensembles (PLOG) resulted in the best performance when used as a base classifier for RSLO. Random hyperplane splits used in RSLO resulted in degeneration of performance at the greatest levels of CV-fold and iteration number when compared with hyperplane splits in principal direction linear oracle (PDLO), which increased with increasing CV-fold and iteration number.","PeriodicalId":448863,"journal":{"name":"Sixth International Conference on Machine Learning and Applications (ICMLA 2007)","volume":"28 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2007-12-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129432228","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
Use of Neural Networks to Predict Adverse Outcomes from Acute Coronary Syndrome for Male and Female Patients 使用神经网络预测男性和女性急性冠脉综合征患者的不良后果
Sixth International Conference on Machine Learning and Applications (ICMLA 2007) Pub Date : 2007-12-13 DOI: 10.1109/ICMLA.2007.40
C. McCullough, Andy Novobilski, F. Fesmire
{"title":"Use of Neural Networks to Predict Adverse Outcomes from Acute Coronary Syndrome for Male and Female Patients","authors":"C. McCullough, Andy Novobilski, F. Fesmire","doi":"10.1109/ICMLA.2007.40","DOIUrl":"https://doi.org/10.1109/ICMLA.2007.40","url":null,"abstract":"Neural networks have been used to examine a set of thirteen objective features and a single subjective physician's assessment for emergency room patients with symptoms possibly indicative of acute coronary syndrome (ACS). The objective data is information routinely collected during triage. The neural networks were used to fuse the disparate types of information with the goal of forecasting thirty-day adverse patient outcome. Results were evaluated using receiver operating characteristic curves describing the outcomes of the nets, both using only objective features and including the subjective physician's assessment. These results, based on all patient data, are compared to those obtained using neural networks trained on information from male and female patients separately. While preliminary, the results of this continuing study are significant from the perspective of potential use of the intelligent fusion of biomedical informatics to aid the physician in prescribing treatment necessary to prevent serious adverse outcome from ACS.","PeriodicalId":448863,"journal":{"name":"Sixth International Conference on Machine Learning and Applications (ICMLA 2007)","volume":"28 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2007-12-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134575081","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: 10
Learning Algorithms for Grammars of Variable Arity Trees 变密度树语法的学习算法
Sixth International Conference on Machine Learning and Applications (ICMLA 2007) Pub Date : 2007-12-13 DOI: 10.1109/ICMLA.2007.22
Neetha Sebastian, K. Krithivasan
{"title":"Learning Algorithms for Grammars of Variable Arity Trees","authors":"Neetha Sebastian, K. Krithivasan","doi":"10.1109/ICMLA.2007.22","DOIUrl":"https://doi.org/10.1109/ICMLA.2007.22","url":null,"abstract":"Grammatical Inference is the technique by which a grammar that best describes a given set of input samples is inferred. This paper considers the inference of tree grammars from a set of sample input trees. Inference of grammars for fixed arity trees is well studied, in this paper we extend the method to give algorithms for inference of grammars for variable arity trees. We give algorithms for inference of local, single type and regular grammar and also consider the use of negative samples. The variable arity trees we consider can be used for representation of XML documents and the algorithms we have given can be used for validation as well as for schema inference.","PeriodicalId":448863,"journal":{"name":"Sixth International Conference on Machine Learning and Applications (ICMLA 2007)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2007-12-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130777100","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
Machine Learning Challenges in Chemoinformatics and Drug Screening and Design 机器学习在化学信息学和药物筛选与设计中的挑战
Sixth International Conference on Machine Learning and Applications (ICMLA 2007) Pub Date : 2007-12-13 DOI: 10.1109/ICMLA.2007.125
P. Baldi
{"title":"Machine Learning Challenges in Chemoinformatics and Drug Screening and Design","authors":"P. Baldi","doi":"10.1109/ICMLA.2007.125","DOIUrl":"https://doi.org/10.1109/ICMLA.2007.125","url":null,"abstract":"Informatics and computers have not yet become as pervasive in chemistry as they have in physics and biology. Drawing analogies from bioinformatics, key ingredients for progress in chemoinformatics are the availability of large, annotated databases of compounds and reactions, data structures and algorithms to efficiently search these databases, and computational methods to predict the physical, chemical, and biological properties of new compounds and reactions. We will describe the development of: (1) a large public database of compounds and reactions (ChemDB); (2) machine learning kernel methods to predict molecular properties; and (3) the applications of these methods to drug screening/design problems and the identification of new drug leads against a major disease. More broadly, we will discuss some of the challenges and opportunities for computer science, AI, and machine learning in chemistry. Text Mining and Ontology Applications in Bioinformatics and GIS Shamkant B. Navathe College of Computing Georgia Institute of Technology","PeriodicalId":448863,"journal":{"name":"Sixth International Conference on Machine Learning and Applications (ICMLA 2007)","volume":"165 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2007-12-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121308246","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
Bootstrapping algorithms for an application in the automotive domain 自举算法在汽车领域的应用
Sixth International Conference on Machine Learning and Applications (ICMLA 2007) Pub Date : 2007-12-13 DOI: 10.1109/ICMLA.2007.26
Martin Schierle, Sascha Schulz
{"title":"Bootstrapping algorithms for an application in the automotive domain","authors":"Martin Schierle, Sascha Schulz","doi":"10.1109/ICMLA.2007.26","DOIUrl":"https://doi.org/10.1109/ICMLA.2007.26","url":null,"abstract":"Bootstrapping algorithms for information extraction gained a lot of attention in the scientific community over the past few years. Therefore the approaches used differ in major parts of the algorithms as well as in detail. This paper will give an overview of some variants and will evaluate their use in a real-world problem, the extraction of component names from automotive repair orders.","PeriodicalId":448863,"journal":{"name":"Sixth International Conference on Machine Learning and Applications (ICMLA 2007)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2007-12-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116157018","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: 29
Machine learned regression for abductive DNA sequencing 外展DNA测序的机器学习回归
Sixth International Conference on Machine Learning and Applications (ICMLA 2007) Pub Date : 2007-12-13 DOI: 10.1109/ICMLA.2007.33
D. Thornley, Maxim Zverev, Stavros Petridis
{"title":"Machine learned regression for abductive DNA sequencing","authors":"D. Thornley, Maxim Zverev, Stavros Petridis","doi":"10.1109/ICMLA.2007.33","DOIUrl":"https://doi.org/10.1109/ICMLA.2007.33","url":null,"abstract":"We construct machine learned regressors to predict the behaviour of DNA sequencing data from the fluorescent labelled Sanger method. These predictions are used to assess hypotheses for sequence composition through calculation of likelihood or deviation evidence from the comparison of predictions from the hypothesized sequence with target trace data. We machine learn a means for comparing the measures taken from competing hypotheses for the sequence. This is a machine learned implementation of our proposal for abductive DNA basecalling. The results of the present experiments suggest that neural nets are a more effective means for predicting peak sizes than decision tree regressors, and for assembling evidence for competing hypotheses in this context. This is despite the availability of variance estimates in our decision tree regressors.","PeriodicalId":448863,"journal":{"name":"Sixth International Conference on Machine Learning and Applications (ICMLA 2007)","volume":"14 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2007-12-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130421485","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
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