This page lists both works that have been published about the Accord.NET Framework, as well as published works that have used, reference, or otherwise include a mention to the framework.

Technical reports

  • Souza, C.R., " A Tutorial on Principal Component Analysis with the Accord.NET Framework ". Department of Computing, Federal University of Sao Carlos. arXiv:1210.7463. Technical Report, 2012
    A technical report and a complete tutorial on Principal Component Analysis, a common and popular dimensionality reduction technique found in many real-world solutions. This report aims not only to explain PCA in the same molds as given by Lindsay I Smith, but to present many of the features of the Accord.NET Framework which can help performing this analysis.

Academic publications

The following papers include at least a citation to the framework, reference material published based on the framework or have used standalone code which now has been incorporated in the framework. Some of those have been found using search engines or automatic alerts. If you would like to include your paper, or if your paper has been incorrectly incorporated in this collection, please let me know.

  1. Suryakiran Maruvada, 3-D Hand Gesture Recognition with Different Temporal Behaviors using HMM and Kinect, Master Thesis, University of Magdeburg, 2017.
  2. John Hogland, and Nathaniel Anderson, Function Modeling Improves the Efficiency of Spatial Modeling Using Big Data from Remote Sensing Big Data Cognitive Computing, 2017, 1, 3; doi:10.3390/bdcc1010003
  3. Gérald Rocher Jean-Yves Tigli, and Stéphane Lavirotte, Probabilistic Models Toward Controlling Smart-* Environments, IEEE Access, 2017. [HAL]
  4. Julien Cumin, Jean-Marc Petit, Vasile-Marian Scuturici, Sabina Surdu. Data Exploration with SQL using Machine Learning Techniques. International Conference on Extending Database Technology - EDBT, Mar 2017, Venice, Italy. Proc. 20th International Conference on Extending Database Technology (EDBT), 2017.
  5. A.Y. Denisova, V.V. Sergeyev, " Supervised multichannel image classification algorithm using hierarchical histogram representation", In Procedia Engineering, Volume 201, 2017, Pages 213-222, ISSN 1877-7058,
  6. Xi Y., Cho S., Um K., Cho K. (2015) " Posture Recognition Using Sensing Blocks". In: Park DS., Chao HC., Jeong YS., Park J. (eds) Advances in Computer Science and Ubiquitous Computing. Lecture Notes in Electrical Engineering, vol 373. Springer, Singapore
  7. M. A. Culman et al., "A Novel Application for Identification of Nutrient Deficiencies in Oil Palm Using the Internet of Things," 2017 5th IEEE International Conference on Mobile Cloud Computing, Services, and Engineering (MobileCloud), San Francisco, CA, 2017, pp. 169-172. doi: 10.1109/MobileCloud.2017.32
  8. Pedro Alves de Almeida, Evaluation and Prototypical Implementation of Machine Learning to Detect ECU Misbehavior, Dissertação. Electrical engineering, Electronic engineering, Information engineering, 2017.
  9. Syed H, Jorgensen AL, Morris AP. SurvivalGWAS_Power: a user friendly tool for power calculations in pharmacogenetic studies with “time to event” outcomes. BMC Bioinformatics. 2016;17:523. doi:10.1186/s12859-016-1407-9.
  10. Liebeck, Matthias & Pollack, Philipp & Modaresi, Pashutan & Conrad, Stefan. (2016). HHU at SemEval-2016 Task 1: Multiple Approaches to Measuring Semantic Textual Similarity. 595-601. 10.18653/v1/S16-1090.
  11. Ivan Halim Parmonangan, Jennifer Santoso, Widodo Budiharto, Alexander Agung Santoso Gunawan, " Fast brain control systems for electric wheelchair using support vector machine", Proc. SPIE 10011, First International Workshop on Pattern Recognition, 100111N (11 July 2016); doi: 10.1117/12.2243126;
  12. Penev, Ivaylo. (2016). On the optimum choice of the K Parameter in Hand-Written Digit Recognition by kNN in comparison to SVM. INTERNATIONAL JOURNAL OF NEURAL NETWORKS and ADVANCED APPLICATIONS. 3. 47-52.
  13. Rusiecki, Andrzej, and Mirosław Kordos. " Effectiveness of Unsupervised Training in Deep Learning Neural Networks." Schedae Informaticae 24 (2016): 41-51.
  14. Peter Goldsborough, A Tour of TensorFlow, ArXiv, 2016.
  15. Stefanidis K., Voyiatzis A.G. Kyriakos Stefanidis and Artemios G. Voyiatzis, An HMM-Based Anomaly Detection Approach for SCADA Systems, In: Foresti S., Lopez J. (eds) Information Security Theory and Practice. WISTP 2016. Lecture Notes in Computer Science, vol 9895. Springer, Cham. 2016.
  16. Gérald Rocher, Jean-Yves Tigli, Stéphane Lavirotte. On the Behavioral Drift Estimation of Ubiquitous Computing Systems in Partially Known Environments. 13th Annual International Conference on Mobile and Ubiquitous Systems: Computing, Networking and Services, Nov 2016, Hiroshima, Japan. 2016, Proceeding of the 13th Annual International Conference on Mobile and Ubiquitous Systems: Computing, Networking and Services.
  17. Matthias Liebeck, Philipp Pollack, Pashutan Modaresi, Stefan Conrad. HHU at SemEval-2016 Task 1: Multiple Approaches to Measuring Semantic Textual Similarity. Proceedings of SemEval-2016, pages 595–601, San Diego, California, June 16-17, 2016.
  18. Tomasz Hachaj, Marek R. Ogiela, Human actions recognition on multimedia hardware using angle-based and coordinate-based features and multivariate continuous hidden Markov model classifier , Multimedia Tools and Application, December 2016, Volume 75, Issue 23, pp 16265–16285.
  19. Sandhya N. Dhage, Charanjeet Kaur Raina, A review on Machine Learning Techniques, International Journal on Recent and Innovation Trends in Computing and Communication ISSN: 2321-8169 Volume: 4 Issue: 3 395 - 399, 2016.
  20. Jennifer K Roe, Niclas Thomas, Eliza Gil, Katharine Best, Evdokia Tsaliki, Stephen Morris‑Jones, Sian Stafford, Nandi Simpson, Karolina D Witt, Benjamin Chain, Robert F Miller, Adrian Martineau, and Mahdad Noursadeghi, Blood transcriptomic diagnosis of pulmonary and extrapulmonary tuberculosis, JCI Insight. 2016;1(16):e87238, doi:10.1172/jci.insight.87238.
  21. Alexander Askinadze, Anwendung der Regressions-SVM zur Vorhersage studentischer Leistungen, Proceedings of the 28th GI-Workshop Grundlagen von Datenbanken, Nörten Hardenberg, Germany, May 24-27, 2016.
  22. D. Matsumoto and Y. Kuwahara, Heartbeat and Respiratory Monitoring using Standing Wave Radar and Independet Component Analysis, Proceedings of ISAP2016, Okinawa, Japan, 2016.
  23. Ivan Halim Parmonangana, Jennifer Santosoa, Widodo Budiharto, Alexander Agung Santoso Gunawan, Fast Brain Control Systems for Electric Wheelchair using Suppor Vector Machine, First International Workshop on Pattern Recognition, Proceedings of SPIE, Vol. 10011, 100111N, doi: 10.1117/12.2243126, 2016.
  24. Greg C. Lee, Fu-Hao Yeh, Yi-Han Hsiao, Kinect-based Taiwanese sign-language recognition system, January 2016, Volume 75, Issue 1, pp 261–279
  25. B Nithya, An Analysis on Applications of Machine Learning Tools, Techniques and Practices in Health Care System, International Journal of Advanced Research in Computer Science and Software Engineering, Volume 6, Issue 6, June 2016.
  26. Andreas Larsson, Tony Segeras, Automated invoice handling with machine learning and OCR, Degree project in Computer Engineering, Stockholm, Sweden, 2016.
  27. Hamzah Syed1, Andrea L. Jorgensen1 and Andrew P. Morris, SurvivalGWAS_Power: a user friendly tool for power calculations in pharmacogenetic studies with "time to event" outcomes, BMC Bioinformatics (2016) 17:523, DOI 10.1186/s12859-016-1407-9.
  28. Bc. Martin Tamajka, Segmentation of anatomical organs in medical data Master Thesis. Slovak University of Technology, Faculty of Informatics and Information Technologies, FIIT-5208-46174, 2016.
  29. Cau, Seang Buan, Software Reuse and Its Effect on Software Quality for Real-Time Geometric Measurement, Thesis Submitted to the Faculty of Graduate Studies and Research In Partial Fulfillment of the Requirements for the Degree of Master of Applied Science in Software Systems Engineering, University of Regina. X, 97 p, 2016.
  30. Cooley, Ben. Determining Unique Agents by Evaluating Web Form Interaction. Thesis (open access), Master of Science in Computer Science (M.S.), Spring 2016.
  31. T. Hachaj, M. R. Ogiela and K. Koptyra, "Application of hidden markov models and gesture description language classifiers to Oyama karate techniques recognition," 2015 9th International Conference on Innovative Mobile and Internet Services in Ubiquitous Computing, Blumenau, 2015, pp. 160-165.
  32. Steven Houben, Nicolai Marquardt, WATCHCONNECT: A Toolkit for Prototyping Smartwatch-Centric Cross-Device Applications Proceedings Proceedings of the 33rd Annual ACM Conference on Human Factors in Computing Systems, Pages 1247-1256, Seoul, Republic of Korea — April 18 - 23, 2015
  33. Lin Yu Han, HMM para o Reconhecimento de Caracteres Manuscritos, Universidade Federal do Paraná, 2015.
  34. Tiago Susano Pinto, An Approach of a Research Tool based in a Shamanic Interface, Master's Thesis, Faculdade de Engenharia, Universidade do Porto, 2015.
  35. Ari Freyr Ásgeirsson, Ívar Oddsson, Sigursteinn Bjarni Húbertsson, Valgeir Björnsson, Ævar Ísak Ástþórsson, Thrifter: An online marketplace with a recommender system, B.Sc. in Computer Science, Reykjavik University, Sep 10, 2015.
  36. Pinto, Tiago Susano [et al.] - A game as a tool for empirical research on the shamanic interface concept. In SciTecIn15. Conferência Ciências e Tecnologias da Interação, Coimbra, 2015.
  37. Simon Ruffieux, Denis Lalanne, Elena Mugellini, Omar Abou Khaled, Gesture recognition corpora and tools: A scripted ground truthing method . Computer Vision and Image Understanding, Volume 131, February 2015, Pages 72-87, ISSN 1077-3142, 2014.
  38. Pongsametrey Sok, Nguonly Taing, Support Vector Machine (SVM) Based Classifier For Khmer Printed Character-set Recognition, 2014.
  39. Emil Lunkdvist, Decision Tree Classification and Forecasting of Pricing Time Series Data, Master's Degree Project, KTH Electrical Engineering, Stockholm, Sweden July 2014.
  40. Bartosz Papis, Evaluation of Colour Recognition Algorithms with a Palette Designed for Applications which Aid People with Visual Impairment . In: International Journal of Image, Graphics and Signal Processing (IJIGSP), DOI: 10.5815/ijigsp.2014.12.01, 2014.
  41. T. Roushan, D. Chaki, A. M. H. Ali, Predicting Untranslated Regions and Code Sections in DNA using Hidden Markov Models . International Journal of Computer and Information Technology (ISSN: 2279 – 0764), Volume 03 – Issue 05, September 2014.
  42. Hogland, John S.; Anderson, Nathaniel M. Improved analyses using function datasets and statistical modeling . In: Proceedings of the 2014 ESRI Users Conference; July 14-18, 2014, San Diego, CA. Redlands, CA: Environmental Systems Research Institute. 2014.
  43. Laxpati, Nealen G. et al. Real-Time in Vivo Optogenetic Neuromodulation and Multielectrode Electrophysiologic Recording with NeuroRighter . Frontiers in Neuroengineering 7 (2014): 40. PMC. Web. 24 Dec. 2014.
  44. Svilen Dimitrov, Framework for Analyzing Sounds of Home Environment for Device Recognition . Department of Computer Science at Saarland University, German Research Center for Artificial Intelligence - DFKI. Master's thesis, 2014.
  45. Michael D. Moore, Development and Resultant Implementation of Custom Software for Analyses of Galactic Neighborhoods . West Virginia University. Master thesis, 179 pages, 2014.
  46. Veyel D, Erban A, Fehrle I, Kopka J, Schroda M. Rationales and Approaches for Studying Metabolism in Eukaryotic Microalgae . Metabolites; 4(2):184-217. 2014.
  47. Karimi, Behnam. An automatic system for classification of breast cancer lesions in ultrasound images . PhD thesis, Concordia University, 2014.
  48. Boffo, Sandra: Assistenzsysteme mit Emotionserkennung. Prototypische Realisierung mit Betrachtung der ethischen Dimension . University of Stuttgart, Faculty of Computer Science, Electrical Engineering, and Information Technology, Student Thesis No. 2450 (2014).
  49. N. Dhaubhadel, S. Subedi, S. Sagar Dongol, ANFA Database and Prediction System , Group project for the Bachelor of Computer Science and Information Technology, St. Xavier‘s College. Maitighar, Kathmandu, 2014.
  50. Steenbergen, N. Predicting glucose concentration in type 1 diabetes patients using artificial neural networks . Faculty of Humanities Theses, Bachelor thesis, 2014.
  51. Z. Zhong, Y. Peng, X. Li, D. Zhou, Color Balance and Seam-line Removal for Multi-camera Stitching Image , JNIT: Journal of Next Generation Information Technology, Vol. 5, No. 2, pp. 47 ~ 55, 2014
  52. M. Kolařík, R. Jašek, Z. K. Oplatková, Maximizing Vector Distances for Purpose of Searching—A Study of Differential Evolution Suitability , Proceedings of the Fifth International Conference on Innovations in Bio-Inspired Computing and Applications IBICA 2014 Advances in Intelligent Systems and Computing Volume 303, 2014, pp 419-428.
  53. A. G. Alex, S. Jegatha, J. G. Jayanthi; S. A. Rabara, SaaS Framework for Library Augmented Reality Application , 2014 World Congress on Computing and Communication Technologies (WCCCT), 2014.
  54. Madis Vellamae, Tomonori Hashiyama, Intelligent Living Room System Which Learns Human Activities, The 29th Fuzzy System Symposium (Osaka, September 9-11, 2013).
  55. Phua, Yee Ling. Social media sentiment analysis and topic detection for Singapore English . Monterey, California. Naval Postgraduate School. Master thesis, September 2013.
  56. H. Josiński, A. Świtoński, A. Michalczuk, D. Kostrzewa, K. Wojciechowski. Human Identification Based on Gait Video Sequences , In: Proceedings of the International Conference on Computer Science and Engineering, p. 312-317, 2013
  57. Daniel Thuerck, Developing an Linguistic Forensics System and Providing useful NLP Data Visualizations . Lab Project - Final Report, Oct. 9, 2013.
  58. H. Josiński, D. Kostrzewa, A. Michalczuk, A. Świtoński, K. Wojciechowski. Feature Extraction and HMM-based Classification of Gait Video Sequences for the Purpose of Human Identification . In: Vision Based Systems for UAV Applications. Studies in Computational Intelligence Volume 481, 2013, pp 233-245.
  59. C. Schneider, A. Barker, and S. Dobson. Autonomous Fault Detection in Self-Healing Systems: Comparing Hidden Markov Models and Artificial Neural Networks . In Proceedings of International Workshop on Adaptive Self-tuning Computing Systems (ADAPT '14). ACM, New York, NY, USA, 2014.
  60. B. Blamey, T. Crick, G. Oatley. The First Day of Summer: Parsing Temporal Expressions with Distributed Semantics . Research and Development in Intelligent Systems XXX, pp 389-402, 2013. DOI: 10.1007/978-3-319-02621-3_29
  61. L. Angelini, F. Carrino, S. Carrino, M. Caon, D. Lalanne, O. A. Khaled, and E. Mugellini. Opportunistic synergy: a classifier fusion engine for micro-gesture recognition . Proceedings of the 5th International Conference on Automotive User Interfaces and Interactive Vehicular Applications (AutomotiveUI), Pages 30-37, ACM New York, NY, USA, 2013. DOI: 10.1145/2516540.2516563
  62. W. Mueller, K. Nowakowski, R. J. Tomczak, S. Kujawa, J. Rudowicz-Nawrocka, P. Idziaszek, A. Zawadzki; IT system supporting acquisition of image data used in the identification of grasslands . Proc. SPIE 8878, Fifth International Conference on Digital Image Processing (ICDIP 2013), 88781T (July 19, 2013); doi:10.1117/12.2031602.
  63. Senn, Fabian and Lam, Cyrill. Selbstlernende Software zur Analyse von Texten . Bachelor thesis, HSR Hochschule für Technik Rapperswil (2013).
  64. J. G. Arriaga, G. Kossan, M. L. Cody, E. E. Vallejo, C. E. Taylor. Acoustic sensor arrays for understanding bird communication. Identifying Cassin's Vireos using SVMs and HMMs. ADVANCES IN ARTIFICIAL LIFE, ECAL 2013 DOI:
  65. Hossein Javidnia, Mohammad Amiri and Seyed Iman Meshkat. Article: H.M.C.R Fingerprint Matching. International Journal of Computer Applications 65(6):16-24, March 2013. Published by Foundation of Computer Science, New York, USA.
  66. Keramitsoglou, I.; Kiranoudis, C.T.; Qihao Weng, "Downscaling Geostationary Land Surface Temperature Imagery for Urban Analysis," Geoscience and Remote Sensing Letters, IEEE , vol.10, no.5, pp.1253,1257, Sept. 2013 DOI: 10.1109/LGRS.2013.2257668
  67. M. H. Afif, A-R. Hedar, T. H. A. Hamid, Y. B. Mahdy; SS-SVM (3SVM): A New Classification Method for Hepatitis Disease Diagnosis; International Journal of Advanced Computer Science and Applications, Vol. 4, No. 2, 2013
  68. M. Lidegaard. Development of a Head Mounted Device for Point-of-Gaze Estimation in Three Dimensions, Master thesis. University of Southern Dernmark, 2012.
  69. Caon, M.; Tscherrig, J.; Yong Yue; Khaled, O.A.; Mugellini, E., "Extending the interaction area for view-invariant 3D gesture recognition," Image Processing Theory, Tools and Applications (IPTA), 2012 3rd International Conference on , vol., no., pp.293,298, 15-18 Oct. 2012
  70. M. Afif, A-R. Hedar, Data Classification Using Support Vector Machine Integrated With Scatter Search Method, Proceedings of the 2012 Japan-Egypt Conference on Electronics, Communications and Computers (JEC-ECC 2012), Article number 6186977, Pages 168-172, Alexandria, 6-9 March 2012.
  71. M. Afif, A-R. Hedar, T. H. Abdel Hamid, Y. B. Mahdy, Support Vector Machines With Weighted Powered Kernels For Data Classification, The first International Conference on Advanced Machine Learning Technologies and Applications (AMLTA12), 2012. Available on
  72. M. Afif, A-R. Hedar, T. H. Abdel Hamid, Y. B. Mahdy, Parameter Determination Of Support Vector Machine Using Scatter Search Approach, The 22nd International Conference on Computer Theory and Applications (ICCTA 2012), 2012.
  73. Eli T. Brown, Jingjing Liu, Carla E. Brodley, Remco Chang. Dis-Function: Learning Distance Functions Interactively. To appear in the Proceedings of the IEEE Conference on Visual Analytics Science and Technology, 2012.
  74. Guido Soetens, Estimating the limitations of single-handed multi-touch input. Master Thesis, Utrecht University. September, 2012.
  75. K. N. Pushpalatha, A. K. Gautham, D. R. Shashikumar, K. B. ShivaKumar. Iris Recognition System with Frequency Domain Features optimized with PCA and SVM Classifier, IJCSI International Journal of Computer Science Issues, Vol. 9, Issue 5, No 1, September 2012.
  76. Reza Taherkhani, Mohammad Kia. Designing a high accuracy 3D auto stereoscopic eye tracking display, using a common LCD monitor. Journal of 3D Research, July 2012, 3:2.
  77. Arnaud Ogier, Thierry Dorval. HCS-Analyzer: Open source software for High-Content Screening data correction and analysis. Bioinformatics. First published online May 13, 2012.
  78. Ludovico Buffon, Evelina Lamma, Fabrizio Riguzzi, and Davide Forment. Un sistema di vision inspection basato su reti neurali. In Matteo Baldoni, Federico Chesani, Bernardo Magnini, Paola Mello, and Marco Montai, editors, Popularize Artificial Intelligence. Proceedings of the AI*IA Workshop and Prize for Celebrating 100th Anniversary of Alan Turing's Birth (PAI 2012), Rome, Italy, June 15, 2012, number 860 in CEUR Workshop Proceedings, pages 1-6, Aachen, Germany, 2012.
  79. Liam Williams, Spotting The Wisdom In The Crowds. Master Thesis on Joint Mathematics and Computer Science. Imperial College London, Department of Computing. June, 2012.
  80. Yaser Alosefer, Analysing Web-based Malware Behaviour through Client Honeypots. PhD Thesis. Cardiff University, School of Computer Science & Informatics, 2012.
  81. Le Hoang Thai,Tran Son Hai,Nguyen Thanh Thuy, "Image Classification using Support Vector Machine and Artificial Neural Network", IJITCS, vol.4, no.5, pp.32-38, 2012.
  82. Zavalnijs, A. Improving Interaction in a Musical Tutor for Playing by Ear . Master Thesis. School of Informatics, University of Edinburgh, 2011.
  83. D. A. Logsdon. Arm-Hand-Finger Video Game Interaction . Master's thesis, Texas A&M University, 2011.
  84. Almeida, R. N., Portuguese Sign Language Recognition via Computer Vision and Depth Sensor. Master Thesis. Lisbon University Institute, Department of Science and Information Technology. October, 2011.
  85. Alosefer, Y.; Rana, O.F.; "Predicting client-side attacks via behaviour analysis using honeypot data," Next Generation Web Services Practices (NWeSP), 2011 7th International Conference on , vol., no., pp.31-36, 19-21 Oct. 2011
  86. Brummitt, L. Scrabble Referee: Word Recognition Component, 2011. Final project report. University of Sheffield, Sheffield, England.
  87. Cani, V., 2011. Image Stitching for UAV remote sensing application. Master Degree Thesis. Computer Engineering, School of Castelldefels of Universitat Politècnica de Catalunya. Barcelona, Spain.
  88. Feuerstack, S., Colnago, J. H. and Souza, C. R.; Designing and Executing Multimodal Interfaces for the Web based on State Chart XML. Proceedings of 3a. Conferência Web W3C Brasil 2011, The 3rd W3C Brazil Web Conference, 17th-18th November, Rio de Janeiro, Brazil. 2011.
  89. Hassani, A. Z.; "Touch versus in-air Hand Gestures: Evaluating the acceptance by seniors of Human-Robot Interaction using Microsoft Kinect," Master Thesis, University of Twente, Enschede, Netherlands, 2011.
  90. Kaplan, K., 2011. ADES: Automatic Driver Evaluation System. PhD Thesis, Boğaziçi University, Istanbul, Turkey.
  91. Wright, M., Lin, C.-J., O'Neill, E., Cosker, D. and Johnson, P., 2011. 3D Gesture recognition: An evaluation of user and system performance. In: Pervasive Computing - 9th International Conference, Pervasive 2011, Proceedings. Heidelberg: Springer Verlag, pp. 294-313.
  92. Deinhofer, M., Roland L., Baca, A., 2010. Klassifizierung von Schlagtechniken im Badminton mittels Mustererkennung aus Inertialdaten. 13 Österreichische Sportwissenschaftliche Gesellschaft, Bruch an der Mur.
  93. Lourenço, J., 2010. Wii3D: Extending the Nintendo Wii Remote into 3D. Final course project report, Rhodes University, Grahamstown. 110p.
  94. Mendelssohn, T.; 2010. Gestureboard - Entwicklung eines Wiimote-basierten, gestengesteuerten, Whiteboard-Systems für den Bildungsbereich. Final project report. Hochschule Furtwangen University, Furtwangen im Schwarzwald, Germany.

Standalone software and projects

  • Virtual Beach 3 - Virtual Beach 3 is a is a decision support tool created by the United States Enviromental Protection Agency to construct site-specific statistical models to predict bacteria (FIB) concentrations at recreational beaches. The framework is listed as one of the technologies employed in its development.
  • PhotoCam by Gauss Development - PhotoCam software helps users choose the best eyeglasses. It is installed on specially made computers (panels with touch screens) in optical stores.
  • Point and Call - Windows phone application to read a phone number using the phone's camera. After reading the number, it is possible to either call or save this number in your contact list, useful for reading numbers from magazines or business cards.
  • Harperia - Language agnostic speech recognition. Uses AForge.NET and Accord.NET to process audio signals and recognize specific recordings using Neural Networks.
  • GMM-EM - Estimates parameters of a multidimensional Gaussian Mixture Model (GMM) by expectation-Maximization (EM) methods and performs Receiver Operating Characteristics (ROC) analysis based on given true labels of input vectors in medical research. Created in a time where GMMs were not yet available in Accord.NET, this project uses a combination of many libraries to achieve its goals.

Online resources

  • Haar-feature Object Detection in C# - A description of how it was possible to achieve real-time face detection with some clever ideas back in 2001.
  • Handwriting Recognition using Kernel Discriminant Analysis - Demonstration of handwritten digit recognition using Kernel Discriminant Analysis and the optical recognition of handwritten digits data set from the UCI Machine Learning Repository.
  • Handwriting Recognition Revisited: Kernel Support Vector Machines - In a previous article, we discussed how to perform the recognition of handwritten digits using Kernel Discriminant Analysis. In this article, we will discuss some techniques to do it using Kernel Support Vector Machines.
  • Automatic Image Stitching with Accord.NET - Demonstration of automatic image stitching by interest point matching using the Accord and AForge.NET Frameworks
  • Decision Trees in C# - Implementing and using Decision Trees in C#.
  • Gaussian Mixture Models and Expectation-Maximization - Gaussian Mixture Models (GMM) can be seen as a type of unsupervised learning or clustering methods. They are among the most statistically mature methods for clustering. But unlike K-Means, GMMs are able to build soft clustering boundaries, i.e., points in space can belong to any class with a given probability.
  • Kernel Principal Component Analysis in C# - KPCA is an extension of principal component analysis (PCA) using techniques of kernel methods. Using a kernel, the originally linear operations of PCA are done in a reproducing kernel Hilbert space with a non-linear mapping. This article details the method and its implementation in the Accord.NET Framework.

Preferred citation style

If you use Accord.NET Framework in your research, you are invited to cite the project in your publication. There are many advantages in clearly stating all tools used in a research, including the increased reproducibility and transparency which should be the goal of any serious academic publication. If you would like to accept this invitation and cite Accord.NET or any material published together the framework in your work, the preferred style of citation is:

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