Pub Date : 1900-01-01DOI: 10.3233/978-1-61499-330-8-25
M. Ashcroft
The purpose of this paper is to present a simple extension to an existing inference algorithm on influence diagrams (i.e. decision theoretic extensions to Bayesian networks) that permits these algo ...
本文的目的是对现有的影响图推理算法(即贝叶斯网络的决策理论扩展)进行简单的扩展，从而允许这些算法…
{"title":"Performing Decision-Theoretic Inference in Bayesian Network Ensemble Models","authors":"M. Ashcroft","doi":"10.3233/978-1-61499-330-8-25","DOIUrl":"https://doi.org/10.3233/978-1-61499-330-8-25","url":null,"abstract":"The purpose of this paper is to present a simple extension to an existing inference algorithm on influence diagrams (i.e. decision theoretic extensions to Bayesian networks) that permits these algo ...","PeriodicalId":322432,"journal":{"name":"Scandinavian Conference on AI","volume":"16 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121360065","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}
Pub Date : 1900-01-01DOI: 10.3233/978-1-60750-754-3-20
Henrik Boström
The random forest algorithm belongs to the class of ensemble learning methods that are embarassingly parallel, i.e., the learning task can be straightforwardly divided into subtasks that can be sol ...
{"title":"Concurrent Learning of Large-Scale Random Forests","authors":"Henrik Boström","doi":"10.3233/978-1-60750-754-3-20","DOIUrl":"https://doi.org/10.3233/978-1-60750-754-3-20","url":null,"abstract":"The random forest algorithm belongs to the class of ensemble learning methods that are embarassingly parallel, i.e., the learning task can be straightforwardly divided into subtasks that can be sol ...","PeriodicalId":322432,"journal":{"name":"Scandinavian Conference on AI","volume":"35 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132275579","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}
Pub Date : 1900-01-01DOI: 10.3233/978-1-61499-330-8-55
Anton Borg, Niklas Lavesson, V. Boeva
Clustering algorithms have been used to divide genes into groups ac- cording to the degree of their expression similarity. Such a grouping may suggest that the respective genes are correlated and/or co-regulated, and subsequently in- dicates that the genes could possibly share a common biological role. In this pa- per, four clustering algorithms are investigated: k-means, cut-clustering, spectral and expectation-maximization. The algorithms are benchmarked against each other. The performance of the four clustering algorithms is studied on time series expres- sion data using Dynamic Time Warping distance in order to measure similarity be- tween gene expression profiles. Four different cluster validation measures are used to evaluate the clustering algorithms: Connectivity and Silhouette Index for esti- mating the quality of clusters, Jaccard Inde xf or evaluating the stability of ac luster method and Rand Index for assessing the accuracy. The obtained results are ana- lyzed by Friedman's test and the Nemenyi post-hoc test. K-means is demonstrated to be significantly better than the spectral clustering algorithm under the Silhouette and Rand validation indices. Keywords. gene expression data, graph-based clustering algorithm, minimum cut clustering, partitioning algorithm, dynamic time warping
{"title":"Comparison of Clustering Approaches for Gene Expression Data","authors":"Anton Borg, Niklas Lavesson, V. Boeva","doi":"10.3233/978-1-61499-330-8-55","DOIUrl":"https://doi.org/10.3233/978-1-61499-330-8-55","url":null,"abstract":"Clustering algorithms have been used to divide genes into groups ac- cording to the degree of their expression similarity. Such a grouping may suggest that the respective genes are correlated and/or co-regulated, and subsequently in- dicates that the genes could possibly share a common biological role. In this pa- per, four clustering algorithms are investigated: k-means, cut-clustering, spectral and expectation-maximization. The algorithms are benchmarked against each other. The performance of the four clustering algorithms is studied on time series expres- sion data using Dynamic Time Warping distance in order to measure similarity be- tween gene expression profiles. Four different cluster validation measures are used to evaluate the clustering algorithms: Connectivity and Silhouette Index for esti- mating the quality of clusters, Jaccard Inde xf or evaluating the stability of ac luster method and Rand Index for assessing the accuracy. The obtained results are ana- lyzed by Friedman's test and the Nemenyi post-hoc test. K-means is demonstrated to be significantly better than the spectral clustering algorithm under the Silhouette and Rand validation indices. Keywords. gene expression data, graph-based clustering algorithm, minimum cut clustering, partitioning algorithm, dynamic time warping","PeriodicalId":322432,"journal":{"name":"Scandinavian Conference on AI","volume":"221 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133546375","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}
Pub Date : 1900-01-01DOI: 10.3233/978-1-60750-754-3-143
R. Bakken, Odd Erik Gundersen
{"title":"View-Independent Human Gait Recognition Using CBR and HMM","authors":"R. Bakken, Odd Erik Gundersen","doi":"10.3233/978-1-60750-754-3-143","DOIUrl":"https://doi.org/10.3233/978-1-60750-754-3-143","url":null,"abstract":"","PeriodicalId":322432,"journal":{"name":"Scandinavian Conference on AI","volume":"94 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133664723","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}
Pub Date : 1900-01-01DOI: 10.3233/978-1-60750-754-3-193
Terje N. Lillegraven, Arnt C. Wolden, Anders Kofod-Petersen, H. Langseth
The use of computer supported travelling in the tourist industry has been steadily increasing and has recently attracted considerable interest. Tourism is in many ways the domain most closely connected with personal preferences and by definition connected to (physical) mobility. Hence, not surprisingly personalised location-based information systems are very suitable for this domain. The modern tourists do not only require general guidance and information but also information specifically tailored to their personal preferences. Local guides and guided tours cover many tourists’ needs by customising tours. Yet, a location-based personalised recommender systems offers a supplement to the available customised services. Recommender systems are designed to help users cope with vast amounts of information, and they do so by presenting only a certain subset of items that is believed to be relevant for the user. The typical tourist will not linger long in any location. Hence, a location-based information system will not be able to effectively learn the idiosyncrasies of any single tourist. This is a challenge when dealing with recommender systems, as they (most often) rely on a classification of the user and the information it is attempting to recommend. Not having sufficient information to give good recommendations to a new user is known as the cold-start-user problem. The cold-start-user problem can to some degree be alleviated by employing user models. However, building user models requires (sufficient) knowledge about the specific user. Acquiring this knowledge is subject to the knowledge bottleneck problem. That is, it is time consuming (for the user) and not necessarily easily accessible. A key question is therefore what type of information to query from a user, to what extent should information be collected, and how should the user information be exploited when the system gives recommendations. In this abstract we give the conclusions of a structured literature review [3] designed to answer these questions. The literature review focuses attention to CF models combining Bayesian networks with user modelling as a means of mitigating both the cold-start-user and knowledge bottleneck problem.
{"title":"Extended Abstract: A design for a tourist CF system","authors":"Terje N. Lillegraven, Arnt C. Wolden, Anders Kofod-Petersen, H. Langseth","doi":"10.3233/978-1-60750-754-3-193","DOIUrl":"https://doi.org/10.3233/978-1-60750-754-3-193","url":null,"abstract":"The use of computer supported travelling in the tourist industry has been steadily increasing and has recently attracted considerable interest. Tourism is in many ways the domain most closely connected with personal preferences and by definition connected to (physical) mobility. Hence, not surprisingly personalised location-based information systems are very suitable for this domain. The modern tourists do not only require general guidance and information but also information specifically tailored to their personal preferences. Local guides and guided tours cover many tourists’ needs by customising tours. Yet, a location-based personalised recommender systems offers a supplement to the available customised services. Recommender systems are designed to help users cope with vast amounts of information, and they do so by presenting only a certain subset of items that is believed to be relevant for the user. The typical tourist will not linger long in any location. Hence, a location-based information system will not be able to effectively learn the idiosyncrasies of any single tourist. This is a challenge when dealing with recommender systems, as they (most often) rely on a classification of the user and the information it is attempting to recommend. Not having sufficient information to give good recommendations to a new user is known as the cold-start-user problem. The cold-start-user problem can to some degree be alleviated by employing user models. However, building user models requires (sufficient) knowledge about the specific user. Acquiring this knowledge is subject to the knowledge bottleneck problem. That is, it is time consuming (for the user) and not necessarily easily accessible. A key question is therefore what type of information to query from a user, to what extent should information be collected, and how should the user information be exploited when the system gives recommendations. In this abstract we give the conclusions of a structured literature review [3] designed to answer these questions. The literature review focuses attention to CF models combining Bayesian networks with user modelling as a means of mitigating both the cold-start-user and knowledge bottleneck problem.","PeriodicalId":322432,"journal":{"name":"Scandinavian Conference on AI","volume":"28 7 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133737201","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}
Pub Date : 1900-01-01DOI: 10.3233/978-1-61499-330-8-35
M. Bendtsen, J. Peña
Bayesian networks have grown to become a dominant type of model within the domain of probabilistic graphical models. Not only do they empower users with a graphical means for describing the relatio ...
贝叶斯网络已经发展成为概率图模型领域的主要模型类型。它们不仅为用户提供了描述关系的图形方法……
{"title":"Gated Bayesian Networks","authors":"M. Bendtsen, J. Peña","doi":"10.3233/978-1-61499-330-8-35","DOIUrl":"https://doi.org/10.3233/978-1-61499-330-8-35","url":null,"abstract":"Bayesian networks have grown to become a dominant type of model within the domain of probabilistic graphical models. Not only do they empower users with a graphical means for describing the relatio ...","PeriodicalId":322432,"journal":{"name":"Scandinavian Conference on AI","volume":"22 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132135517","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}
Pub Date : 1900-01-01DOI: 10.3233/978-1-60750-754-3-40
G. Ruß, R. Kruse
{"title":"Machine Learning Methods for Spatial Clustering on Precision Agriculture Data","authors":"G. Ruß, R. Kruse","doi":"10.3233/978-1-60750-754-3-40","DOIUrl":"https://doi.org/10.3233/978-1-60750-754-3-40","url":null,"abstract":"","PeriodicalId":322432,"journal":{"name":"Scandinavian Conference on AI","volume":"59 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133713066","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}
Pub Date : 1900-01-01DOI: 10.3233/978-1-61499-589-0-179
J. David, Roland Philippsen
We consider the problem of finding collision-free trajectories for a fleet of automated guided vehicles (AGVs) working in ship ports and freight terminals. Our solution computes collision-free traj ...
{"title":"Task Assignment and Trajectory Planning in Dynamic environments for Multiple Vehicles","authors":"J. David, Roland Philippsen","doi":"10.3233/978-1-61499-589-0-179","DOIUrl":"https://doi.org/10.3233/978-1-61499-589-0-179","url":null,"abstract":"We consider the problem of finding collision-free trajectories for a fleet of automated guided vehicles (AGVs) working in ship ports and freight terminals. Our solution computes collision-free traj ...","PeriodicalId":322432,"journal":{"name":"Scandinavian Conference on AI","volume":"65 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116316222","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}
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