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Dahlbom, Anders
Publications (10 of 27) Show all publications
Torra, V., Narukawa, Y. & Dahlbom, A. (2017). On this book: Clustering, multisets, rough sets and fuzzy sets. In: Vicenç Torra, Anders Dahlbom & Yasuo Narukawa (Ed.), Fuzzy sets, rough sets, multisets and clustering: (pp. 1-5). Springer
Open this publication in new window or tab >>On this book: Clustering, multisets, rough sets and fuzzy sets
2017 (English)In: Fuzzy sets, rough sets, multisets and clustering / [ed] Vicenç Torra, Anders Dahlbom & Yasuo Narukawa, Springer, 2017, p. 1-5Chapter in book (Other academic)
Abstract [en]

This chapter gives an overview of the content of this book, and links them with the work of Prof. Sadaaki Miyamoto, to whom this book is dedicated.

Place, publisher, year, edition, pages
Springer, 2017
Series
Studies in Computational Intelligence, ISSN 1860-949X ; 671
Keywords
Hesitant fuzzy sets, Data mining, Clustering algorithm, Fuzzy clustering
National Category
Computer Sciences
Research subject
Natural sciences; Skövde Artificial Intelligence Lab (SAIL); INF301 Data Science
Identifiers
urn:nbn:se:his:diva-13362 (URN)10.1007/978-3-319-47557-8_1 (DOI)000413720000002 ()2-s2.0-85009982265 (Scopus ID)978-3-319-47556-1 (ISBN)978-3-319-47557-8 (ISBN)
Available from: 2017-02-04 Created: 2017-02-04 Last updated: 2019-02-14Bibliographically approved
Torra, V., Aliahmadipour, L. & Dahlbom, A. (2016). Fuzzy, I-fuzzy, and H-fuzzy partitions to describe clusters. In: Fuzzy Systems (FUZZ-IEEE), 2016 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE): . Paper presented at 2016 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE), Vancouver, Canada, July 24-29, 2016 (pp. 524-530). IEEE
Open this publication in new window or tab >>Fuzzy, I-fuzzy, and H-fuzzy partitions to describe clusters
2016 (English)In: Fuzzy Systems (FUZZ-IEEE), 2016 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE), IEEE, 2016, p. 524-530Conference paper, Published paper (Refereed)
Abstract [en]

In this paper we discuss how three types of fuzzy partitions can be used to describe the results of three types of cluster structures. Standard fuzzy partitions are suitable for centroid based clusters, and I-fuzzy partitions for clusters represented by segments or lines (e.g., c-varieties). In this paper, we introduce hesitant fuzzy partitions. They are suitable for clusters defined by sets of centroids. Because of that, we show that they are useful for hierarchical clustering. We also establish the relationship between hesitant fuzzy partitions and I-fuzzy partitions.

Place, publisher, year, edition, pages
IEEE, 2016
Series
IEEE International Fuzzy Systems Conference. Proceedings, ISSN 1544-5615
Keywords
Cluster structure, Fuzzy partition, Hier-archical clustering, nocv2, Standard fuzzy partition
National Category
Computer Sciences
Research subject
Skövde Artificial Intelligence Lab (SAIL)
Identifiers
urn:nbn:se:his:diva-13360 (URN)10.1109/FUZZ-IEEE.2016.7737731 (DOI)000392150700072 ()2-s2.0-85006751117 (Scopus ID)978-1-5090-0626-7 (ISBN)978-1-5090-0625-0 (ISBN)
Conference
2016 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE), Vancouver, Canada, July 24-29, 2016
Available from: 2017-02-04 Created: 2017-02-04 Last updated: 2018-01-13Bibliographically approved
Tavara, S., Sundell, H. & Dahlbom, A. (2015). Empirical Study of Time Efficiency and Accuracy of Support Vector Machines Using an Improved Version of PSVM. In: Hamid R. Arabnia, Hiroshi Ishii, Kazuki Joe, Hiroaki Nishikawa, Havaru Shouno (Ed.), Proceedings of the 2015 International Conference on Parallel and Distributed Processing Techniques and Applications: PDPTA 2015: Volume 1. Paper presented at PDPTA'15 - The 21st International Conference on Parallel and Distributed Processing Techniques and Applications, Las Vegas, July 27-30, 2015 (pp. 177-183). Printed in the United States of America: CSREA Press, 1
Open this publication in new window or tab >>Empirical Study of Time Efficiency and Accuracy of Support Vector Machines Using an Improved Version of PSVM
2015 (English)In: Proceedings of the 2015 International Conference on Parallel and Distributed Processing Techniques and Applications: PDPTA 2015: Volume 1 / [ed] Hamid R. Arabnia, Hiroshi Ishii, Kazuki Joe, Hiroaki Nishikawa, Havaru Shouno, Printed in the United States of America: CSREA Press, 2015, Vol. 1, p. 177-183Conference paper, Published paper (Refereed)
Abstract [en]

We present a significantly improved implementation of a parallel SVM algorithm (PSVM) together with a comprehensive experimental study. Support Vector Machines (SVM) is one of the most well-known machine learning classification techniques. PSVM employs the Interior Point Method, which is a solver used for SVM problems that has a high potential of parallelism. We improve PSVM regarding its structure and memory management for contemporary processor architectures. We perform a number of experiments and study the impact of the reduced column size p and other important parameters as C and gamma on the class-prediction accuracy and training time. The experimental results show that there exists a threshold between the number of computational cores and the training time, and that choosing an appropriate value of p effects the choice of the C and gamma parameters as well as the accuracy.

Place, publisher, year, edition, pages
Printed in the United States of America: CSREA Press, 2015
Keywords
parallel svm, processor technology, training time
National Category
Computer Sciences
Research subject
Technology; Skövde Artificial Intelligence Lab (SAIL)
Identifiers
urn:nbn:se:his:diva-11644 (URN)1-60132-400-6 (ISBN)1-60132-401-4 (ISBN)1-60132-402-2 (ISBN)
Conference
PDPTA'15 - The 21st International Conference on Parallel and Distributed Processing Techniques and Applications, Las Vegas, July 27-30, 2015
Available from: 2015-10-30 Created: 2015-10-30 Last updated: 2019-01-14Bibliographically approved
Baalsrud Hauge, J. M., Stanescu, I., Arnab, S., Moreno Ger, P., Lim, T., Serrano-Laguna, A., . . . Degano, C. (2015). Learning Analytics Architecture to Scaffold Learning Experience through Technology-based Methods. International Journal of Serious Games, 2(1), 29-44
Open this publication in new window or tab >>Learning Analytics Architecture to Scaffold Learning Experience through Technology-based Methods
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2015 (English)In: International Journal of Serious Games, ISSN 2384-8766, Vol. 2, no 1, p. 29-44Article in journal (Refereed) Published
Abstract [en]

The challenge of delivering personalized learning experiences is often increased by the size of classrooms and online learning communities. Serious Games (SGs) are increasingly recognized for their potential to improve education. However, the issues related to their development and their level of effectiveness can be seriously affected when brought too rapidly into growing online learning communities. Deeper insights into how the students are playing is needed to deliver a comprehensive and intelligent learning framework that facilitates better understanding of learners' knowledge, effective assessment of their progress and continuous evaluation and optimization of the environments in which they learn. This paper discusses current SOTA and aims to explore the potential in the use of games and learning analytics towards scaffolding and supporting teaching and learning experience. The conceptual model (ecosystem and architecture) discussed in this paper aims to highlight the key considerations that may advance the current state of learning analytics, adaptive learning and SGs, by leveraging SGs as an suitable medium for gathering data and performing adaptations.

Place, publisher, year, edition, pages
Serious Games Society, 2015
Keywords
game mechanics, gleaner personalization
National Category
Other Computer and Information Science
Research subject
Skövde Artificial Intelligence Lab (SAIL)
Identifiers
urn:nbn:se:his:diva-11585 (URN)10.17083/ijsg.v2i1.38 (DOI)000218566300003 ()
Available from: 2015-10-06 Created: 2015-10-06 Last updated: 2018-04-16Bibliographically approved
Johansson, U., Köning, R., Brattberg, P., Dahlbom, A. & Riveiro, M. (2015). Mining Trackman Golf Data. In: Hamid R. Arabnia, Leonidas Deligiannidis & Quoc-Nam Tran (Ed.), 2015 International Conference on Computational Science and Computational Intelligence (CSCI): . Paper presented at 2015 International Conference on Computational Science and Computational Intelligence (CSCI), Las Vegas, Nevada, USA, 7-9 December 2015 (pp. 380-385). Los Alamitos, CA: IEEE Computer Society
Open this publication in new window or tab >>Mining Trackman Golf Data
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2015 (English)In: 2015 International Conference on Computational Science and Computational Intelligence (CSCI) / [ed] Hamid R. Arabnia, Leonidas Deligiannidis & Quoc-Nam Tran, Los Alamitos, CA: IEEE Computer Society, 2015, p. 380-385Conference paper, Published paper (Refereed)
Abstract [en]

Recently, innovative technology like Trackman has made it possible to generate data describing golf swings. In this application paper, we analyze Trackman data from 275 golfers using descriptive statistics and machine learning techniques. The overall goal is to find non-trivial and general patterns in the data that can be used to identify and explain what separates skilled golfers from poor. Experimental results show that random forest models, generated from Trackman data, were able to predict the handicap of a golfer, with a performance comparable to human experts. Based on interpretable predictive models, descriptive statistics and correlation analysis, the most distinguishing property of better golfers is their consistency. In addition, the analysis shows that better players have superior control of the club head at impact and generally hit the ball straighter. A very interesting finding is that better players also tend to swing flatter. Finally, an outright comparison between data describing the club head movement and ball flight data, indicates that a majority of golfers do not hit the ball solid enough for the basic golf theory to apply.

Place, publisher, year, edition, pages
Los Alamitos, CA: IEEE Computer Society, 2015
National Category
Computer Sciences
Research subject
Natural sciences; Skövde Artificial Intelligence Lab (SAIL)
Identifiers
urn:nbn:se:his:diva-12251 (URN)10.1109/CSCI.2015.77 (DOI)000380405100068 ()2-s2.0-84964425566 (Scopus ID)978-1-4673-9795-7 (ISBN)
Conference
2015 International Conference on Computational Science and Computational Intelligence (CSCI), Las Vegas, Nevada, USA, 7-9 December 2015
Projects
TIKT 1- GOATS
Note

Project funded by VGR, TIKT collaboration Univ. Skövde and Univ. Borås.

Available from: 2016-05-15 Created: 2016-05-15 Last updated: 2018-03-28Bibliographically approved
Riveiro, M., Dahlbom, A., König, R., Johansson, U. & Brattberg, P. (2015). Supporting Golf Coaching and Swing Instruction with Computer-Based Training Systems. In: Panayiotis Zaphiris & Andri Ioannou (Ed.), Learning and Collaboration Technologies: Second International Conference, LCT 2015, Held as Part of HCI International 2015, Los Angeles, CA, USA, August 2-7, 2015, Proceedings. Paper presented at 17th International Conference on Human-Computer Interaction, Los Angeles, CA, USA, August 2-7, 2015 (pp. 279-290). Springer International Publishing Switzerland, 9192
Open this publication in new window or tab >>Supporting Golf Coaching and Swing Instruction with Computer-Based Training Systems
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2015 (English)In: Learning and Collaboration Technologies: Second International Conference, LCT 2015, Held as Part of HCI International 2015, Los Angeles, CA, USA, August 2-7, 2015, Proceedings / [ed] Panayiotis Zaphiris & Andri Ioannou, Springer International Publishing Switzerland , 2015, Vol. 9192, p. 279-290Conference paper, Published paper (Refereed)
Abstract [en]

Golf is a popular sport around the world. Since an accomplished golf swing is essential for succeeding in this sport, golf players spend a considerable amount of time perfecting their swing. In order to guide the design of future computer-based training systems that support swing instruction, this paper analyzes the data gathered during interviews with golf instructors and participant observations of actual swing coaching sessions. Based on our field work, we describe the characteristics of a proficient swing, how the instructional sessions are normally carried out and the challenges professional instructors face. Taking into account these challenges, we outline which desirable capabilities future computer-based training systems for professional golf instructors should have.

Place, publisher, year, edition, pages
Springer International Publishing Switzerland, 2015
Series
Lecture Notes in Computer Science (LNCS), ISSN 0302-9743 ; 9192
Keywords
swing, golf, modeling
National Category
Engineering and Technology
Research subject
Technology; Skövde Artificial Intelligence Lab (SAIL)
Identifiers
urn:nbn:se:his:diva-11278 (URN)10.1007/978-3-319-20609-7_27 (DOI)000364718200027 ()2-s2.0-84947054922 (Scopus ID)978-3-319-20608-0 (ISBN)978-3-319-20609-7 (ISBN)
Conference
17th International Conference on Human-Computer Interaction, Los Angeles, CA, USA, August 2-7, 2015
Projects
GOATS (TIKT)
Available from: 2015-07-02 Created: 2015-07-02 Last updated: 2018-03-28Bibliographically approved
Torra, V., Narukawa, Y. & Dahlbom, A. (Eds.). (2015). The 12th International Conference on Modeling Decisions for Artificial Intelligence: CD-ROM Proceedings. Paper presented at The 12th International Conference on Modeling Decisions for Artificial Intelligence (MDAI 2015), Skövde, 21-23 September, 2015. MDAI - HiS
Open this publication in new window or tab >>The 12th International Conference on Modeling Decisions for Artificial Intelligence: CD-ROM Proceedings
2015 (English)Conference proceedings (editor) (Refereed)
Place, publisher, year, edition, pages
MDAI - HiS, 2015. p. 173
Keywords
Modeling decisions, artificial intelligence
National Category
Electrical Engineering, Electronic Engineering, Information Engineering Computer Systems
Research subject
Technology; Skövde Artificial Intelligence Lab (SAIL)
Identifiers
urn:nbn:se:his:diva-11734 (URN)978-91-637-9363-9 (ISBN)
Conference
The 12th International Conference on Modeling Decisions for Artificial Intelligence (MDAI 2015), Skövde, 21-23 September, 2015
Available from: 2015-12-02 Created: 2015-12-02 Last updated: 2018-03-28Bibliographically approved
Karlsson, A., Dahlbom, A. & Zhong, H. (2014). Evidential Combination Operators for Entrapment Prediction in Advanced Driver Assistance Systems. In: Foundations of Intelligent Systems: 21st International Symposium, ISMIS 2014, Roskilde, Denmark, June 25-27, 2014. Proceedings. Paper presented at 21st International Symposium, ISMIS 2014, Roskilde, Denmark, June 25-27, 2014 (pp. 194-203).
Open this publication in new window or tab >>Evidential Combination Operators for Entrapment Prediction in Advanced Driver Assistance Systems
2014 (English)In: Foundations of Intelligent Systems: 21st International Symposium, ISMIS 2014, Roskilde, Denmark, June 25-27, 2014. Proceedings, 2014, p. 194-203Conference paper, Published paper (Refereed)
Series
Lecture Notes in Computer Science, ISSN 0302-9743 ; 8502
Keywords
Evidential combination operators, advanced driver assistance systems, Bayesian theory, credal sets, Dempster-Shafer theory
National Category
Computer Sciences
Research subject
Technology; Distributed Real-Time Systems; Skövde Artificial Intelligence Lab (SAIL)
Identifiers
urn:nbn:se:his:diva-9707 (URN)10.1007/978-3-319-08326-1_20 (DOI)2-s2.0-84903591422 (Scopus ID)978-3-319-08325-4 (ISBN)978-3-319-08326-1 (ISBN)
Conference
21st International Symposium, ISMIS 2014, Roskilde, Denmark, June 25-27, 2014
Available from: 2014-08-04 Created: 2014-08-04 Last updated: 2018-03-28Bibliographically approved
Dahlbom, A. & Riveiro, M. (2014). Situation Modeling and Visual Analytics for Decision Support in Sports. In: Slimane Hammoudi, Leszek Maciaszek, José Cordeiro (Ed.), Proceedings of the 16th International Conference on Enterprise Information Systems: Volume 1. Paper presented at 16th International Conference on Enterprise Information Systems, Lisbon, Portugal, April 27-30, 2014 (pp. 539-544). SciTePress
Open this publication in new window or tab >>Situation Modeling and Visual Analytics for Decision Support in Sports
2014 (English)In: Proceedings of the 16th International Conference on Enterprise Information Systems: Volume 1 / [ed] Slimane Hammoudi, Leszek Maciaszek, José Cordeiro, SciTePress, 2014, p. 539-544Conference paper, Published paper (Refereed)
Abstract [en]

High performance is a goal in most sporting activities, for elite athletes as well as for recreational practitioners, and the process of measuring, evaluating and improving performance is one fundamental aspect to why people engage in sports. This is a complex process which possibly involves analyzing large amounts of heterogeneous data in order to apply actions that change important properties for improved outcome. The number of computer based decision support systems in the context of data analysis for performance improvement is scarce. In this position paper we briefly review the literature, and we propose the use of information fusion, situation modeling and visual analytics as suitable tools for supporting decision makers, ranging from recreational to elite, in the process of performance evaluation.

Place, publisher, year, edition, pages
SciTePress, 2014
Keywords
Sports, Decision Support, Situation Modeling, Visual Analytics, Information Fusion
National Category
Computer Sciences
Research subject
Technology; Skövde Artificial Intelligence Lab (SAIL)
Identifiers
urn:nbn:se:his:diva-9101 (URN)10.5220/0004973105390544 (DOI)2-s2.0-84902355936 (Scopus ID)978-989-758-027-7 (ISBN)
Conference
16th International Conference on Enterprise Information Systems, Lisbon, Portugal, April 27-30, 2014
Projects
Golf data analysis (GOATS)
Available from: 2014-06-03 Created: 2014-05-22 Last updated: 2018-12-27Bibliographically approved
Dahlbom, A., Riveiro, M., König, R., Johansson, U. & Brattberg, P. (2014). Supporting Golf Coaching with 3D Modeling of Swings. In: Arnold Baca & Michael Stöckl (Ed.), Arnold Baca & Michael Stöckl (Ed.), Sportinformatik X: Jahrestagung der dvs-Sektion Sportinformatik vom 10.-12. September 2014 in Wien. Paper presented at Jahrestagung der dvs-Sektion Sportinformatik vom 10.-12. September 2014 in Wien (pp. 142-148). Paper presented at Jahrestagung der dvs-Sektion Sportinformatik vom 10.-12. September 2014 in Wien. Hamburg: Feldhaus Verlag GmbH & Co. KG
Open this publication in new window or tab >>Supporting Golf Coaching with 3D Modeling of Swings
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2014 (English)In: Sportinformatik X: Jahrestagung der dvs-Sektion Sportinformatik vom 10.-12. September 2014 in Wien / [ed] Arnold Baca & Michael Stöckl, Hamburg: Feldhaus Verlag GmbH & Co. KG , 2014, p. 142-148Chapter in book (Refereed)
Place, publisher, year, edition, pages
Hamburg: Feldhaus Verlag GmbH & Co. KG, 2014
Series
Schriften der Deutschen Vereinigung für Sportwissenschaft, ISSN 1430-2225 ; 244
Keywords
golf kinect 3d modeling swings
National Category
Computer Sciences
Research subject
Natural sciences; Technology; Skövde Artificial Intelligence Lab (SAIL)
Identifiers
urn:nbn:se:his:diva-11584 (URN)978-3-88020-622-9 (ISBN)
Conference
Jahrestagung der dvs-Sektion Sportinformatik vom 10.-12. September 2014 in Wien
Projects
Golf Data Analysis (GOATS)
Available from: 2015-10-06 Created: 2015-10-06 Last updated: 2018-03-28Bibliographically approved
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