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Dahlbom, Anders
Publikasjoner (10 av 27) Visa alla publikasjoner
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
Åpne denne publikasjonen i ny fane eller vindu >>On this book: Clustering, multisets, rough sets and fuzzy sets
2017 (engelsk)Inngår i: Fuzzy sets, rough sets, multisets and clustering / [ed] Vicenç Torra; Anders Dahlbom; Yasuo Narukawa, Springer, 2017, s. 1-5Kapittel i bok, del av antologi (Annet vitenskapelig)
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.

sted, utgiver, år, opplag, sider
Springer, 2017
Serie
Studies in Computational Intelligence, ISSN 1860-949X, E-ISSN 1860-9503 ; 671
Emneord
Hesitant fuzzy sets, Data mining, Clustering algorithm, Fuzzy clustering
HSV kategori
Forskningsprogram
Skövde Artificial Intelligence Lab (SAIL); INF301 Data Science
Identifikatorer
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)
Tilgjengelig fra: 2017-02-04 Laget: 2017-02-04 Sist oppdatert: 2024-02-05bibliografisk kontrollert
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
Åpne denne publikasjonen i ny fane eller vindu >>Fuzzy, I-fuzzy, and H-fuzzy partitions to describe clusters
2016 (engelsk)Inngår i: Fuzzy Systems (FUZZ-IEEE), 2016 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE), IEEE, 2016, s. 524-530Konferansepaper, Publicerat paper (Fagfellevurdert)
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.

sted, utgiver, år, opplag, sider
IEEE, 2016
Serie
IEEE International Fuzzy Systems Conference. Proceedings, ISSN 1544-5615
Emneord
Cluster structure, Fuzzy partition, Hier-archical clustering, nocv2, Standard fuzzy partition
HSV kategori
Forskningsprogram
Skövde Artificial Intelligence Lab (SAIL)
Identifikatorer
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)
Konferanse
2016 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE), Vancouver, Canada, July 24-29, 2016
Tilgjengelig fra: 2017-02-04 Laget: 2017-02-04 Sist oppdatert: 2018-01-13bibliografisk kontrollert
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
Åpne denne publikasjonen i ny fane eller vindu >>Empirical Study of Time Efficiency and Accuracy of Support Vector Machines Using an Improved Version of PSVM
2015 (engelsk)Inngår i: 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, s. 177-183Konferansepaper, Publicerat paper (Fagfellevurdert)
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.

sted, utgiver, år, opplag, sider
Printed in the United States of America: CSREA Press, 2015
Emneord
parallel svm, processor technology, training time
HSV kategori
Forskningsprogram
Teknik; Skövde Artificial Intelligence Lab (SAIL)
Identifikatorer
urn:nbn:se:his:diva-11644 (URN)1-60132-400-6 (ISBN)1-60132-401-4 (ISBN)1-60132-402-2 (ISBN)
Konferanse
PDPTA'15 - The 21st International Conference on Parallel and Distributed Processing Techniques and Applications, Las Vegas, July 27-30, 2015
Tilgjengelig fra: 2015-10-30 Laget: 2015-10-30 Sist oppdatert: 2023-02-01bibliografisk kontrollert
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
Åpne denne publikasjonen i ny fane eller vindu >>Learning Analytics Architecture to Scaffold Learning Experience through Technology-based Methods
Vise andre…
2015 (engelsk)Inngår i: International Journal of Serious Games, ISSN 2384-8766, Vol. 2, nr 1, s. 29-44Artikkel i tidsskrift (Fagfellevurdert) 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.

sted, utgiver, år, opplag, sider
Serious Games Society, 2015
Emneord
game mechanics, gleaner personalization
HSV kategori
Forskningsprogram
Skövde Artificial Intelligence Lab (SAIL)
Identifikatorer
urn:nbn:se:his:diva-11585 (URN)10.17083/ijsg.v2i1.38 (DOI)000218566300003 ()
Tilgjengelig fra: 2015-10-06 Laget: 2015-10-06 Sist oppdatert: 2018-04-16bibliografisk kontrollert
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
Åpne denne publikasjonen i ny fane eller vindu >>Mining Trackman Golf Data
Vise andre…
2015 (engelsk)Inngår i: 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, s. 380-385Konferansepaper, Publicerat paper (Fagfellevurdert)
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.

sted, utgiver, år, opplag, sider
Los Alamitos, CA: IEEE Computer Society, 2015
HSV kategori
Forskningsprogram
Naturvetenskap; Skövde Artificial Intelligence Lab (SAIL)
Identifikatorer
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)
Konferanse
2015 International Conference on Computational Science and Computational Intelligence (CSCI), Las Vegas, Nevada, USA, 7-9 December 2015
Prosjekter
TIKT 1- GOATS
Merknad

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

Tilgjengelig fra: 2016-05-15 Laget: 2016-05-15 Sist oppdatert: 2018-03-28bibliografisk kontrollert
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
Åpne denne publikasjonen i ny fane eller vindu >>Supporting Golf Coaching and Swing Instruction with Computer-Based Training Systems
Vise andre…
2015 (engelsk)Inngår i: 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, s. 279-290Konferansepaper, Publicerat paper (Fagfellevurdert)
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.

sted, utgiver, år, opplag, sider
Springer International Publishing Switzerland, 2015
Serie
Lecture Notes in Computer Science (LNCS), ISSN 0302-9743, E-ISSN 1611-3349 ; 9192
Emneord
swing, golf, modeling
HSV kategori
Forskningsprogram
Teknik; Skövde Artificial Intelligence Lab (SAIL)
Identifikatorer
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)
Konferanse
17th International Conference on Human-Computer Interaction, Los Angeles, CA, USA, August 2-7, 2015
Prosjekter
GOATS (TIKT)
Tilgjengelig fra: 2015-07-02 Laget: 2015-07-02 Sist oppdatert: 2022-09-28bibliografisk kontrollert
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
Åpne denne publikasjonen i ny fane eller vindu >>The 12th International Conference on Modeling Decisions for Artificial Intelligence: CD-ROM Proceedings
2015 (engelsk)Konferanseproceedings (Fagfellevurdert)
sted, utgiver, år, opplag, sider
MDAI - HiS, 2015. s. 173
Emneord
Modeling decisions, artificial intelligence
HSV kategori
Forskningsprogram
Teknik; Skövde Artificial Intelligence Lab (SAIL)
Identifikatorer
urn:nbn:se:his:diva-11734 (URN)978-91-637-9363-9 (ISBN)
Konferanse
The 12th International Conference on Modeling Decisions for Artificial Intelligence (MDAI 2015), Skövde, 21-23 September, 2015
Tilgjengelig fra: 2015-12-02 Laget: 2015-12-02 Sist oppdatert: 2018-03-28bibliografisk kontrollert
Karlsson, A., Dahlbom, A. & Zhong, H. (2014). Evidential Combination Operators for Entrapment Prediction in Advanced Driver Assistance Systems. In: Troels Andreasen; Henning Christiansen; Juan-Carlos Cubero; Zbigniew W. Raś (Ed.), 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). Springer International Publishing Switzerland
Åpne denne publikasjonen i ny fane eller vindu >>Evidential Combination Operators for Entrapment Prediction in Advanced Driver Assistance Systems
2014 (engelsk)Inngår i: Foundations of Intelligent Systems: 21st International Symposium, ISMIS 2014, Roskilde, Denmark, June 25-27, 2014. Proceedings / [ed] Troels Andreasen; Henning Christiansen; Juan-Carlos Cubero; Zbigniew W. Raś, Springer International Publishing Switzerland , 2014, s. 194-203Konferansepaper, Publicerat paper (Fagfellevurdert)
Abstract [en]

We propose the use of evidential combination operators for advanced driver assistance systems (ADAS) for vehicles. More specifically, we elaborate on how three different operators, one precise and two imprecise, can be used for the purpose of entrapment prediction, i.e., to estimate when the relative positions and speeds of the surrounding vehicles can potentially become dangerous. We motivate the use of the imprecise operators by their ability to model uncertainty in the underlying sensor information and we provide an example that demonstrates the differences between the operators.

sted, utgiver, år, opplag, sider
Springer International Publishing Switzerland, 2014
Serie
Lecture Notes in Computer Science, ISSN 0302-9743, E-ISSN 1611-3349 ; 8502
Emneord
Evidential combination operators, advanced driver assistance systems, Bayesian theory, credal sets, Dempster-Shafer theory
HSV kategori
Forskningsprogram
Teknik; Distribuerade realtidssystem (DRTS); Skövde Artificial Intelligence Lab (SAIL)
Identifikatorer
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)
Konferanse
21st International Symposium, ISMIS 2014, Roskilde, Denmark, June 25-27, 2014
Forskningsfinansiär
Knowledge Foundation, 2010-0320
Merknad

Springer Cham

This work was supported by the Information Fusion Research Program (University of Skövde, Sweden), in partnership with the Swedish Knowledge Foundation under grant 2010-0320 (URL: http://www.infofusion.se, UMIF project).

Tilgjengelig fra: 2014-08-04 Laget: 2014-08-04 Sist oppdatert: 2023-03-24bibliografisk kontrollert
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
Åpne denne publikasjonen i ny fane eller vindu >>Situation Modeling and Visual Analytics for Decision Support in Sports
2014 (engelsk)Inngår i: Proceedings of the 16th International Conference on Enterprise Information Systems: Volume 1 / [ed] Slimane Hammoudi, Leszek Maciaszek, José Cordeiro, SciTePress, 2014, s. 539-544Konferansepaper, Publicerat paper (Fagfellevurdert)
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.

sted, utgiver, år, opplag, sider
SciTePress, 2014
Emneord
Sports, Decision Support, Situation Modeling, Visual Analytics, Information Fusion
HSV kategori
Forskningsprogram
Teknik; Skövde Artificial Intelligence Lab (SAIL)
Identifikatorer
urn:nbn:se:his:diva-9101 (URN)10.5220/0004973105390544 (DOI)2-s2.0-84902355936 (Scopus ID)978-989-758-027-7 (ISBN)
Konferanse
16th International Conference on Enterprise Information Systems, Lisbon, Portugal, April 27-30, 2014
Prosjekter
Golf data analysis (GOATS)
Tilgjengelig fra: 2014-06-03 Laget: 2014-05-22 Sist oppdatert: 2018-12-27bibliografisk kontrollert
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
Åpne denne publikasjonen i ny fane eller vindu >>Supporting Golf Coaching with 3D Modeling of Swings
Vise andre…
2014 (engelsk)Inngår i: 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, s. 142-148Kapittel i bok, del av antologi (Fagfellevurdert)
sted, utgiver, år, opplag, sider
Hamburg: Feldhaus Verlag GmbH & Co. KG, 2014
Serie
Schriften der Deutschen Vereinigung für Sportwissenschaft, ISSN 1430-2225 ; 244
Emneord
golf kinect 3d modeling swings
HSV kategori
Forskningsprogram
Naturvetenskap; Teknik; Skövde Artificial Intelligence Lab (SAIL)
Identifikatorer
urn:nbn:se:his:diva-11584 (URN)978-3-88020-622-9 (ISBN)
Konferanse
Jahrestagung der dvs-Sektion Sportinformatik vom 10.-12. September 2014 in Wien
Prosjekter
Golf Data Analysis (GOATS)
Tilgjengelig fra: 2015-10-06 Laget: 2015-10-06 Sist oppdatert: 2018-03-28bibliografisk kontrollert
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