I thought this was Renee's website! (Maybe hers is becomingdatascience.com?) Most machine learning (ML) models use samples / examples observations as input. This data lacks any time dimension. Time-series forecasting models are...
s.a. IBM's Blue CRUSH & PredPol; Patterns inherent in past crimes (type, place, and time) provide ample info for predictions, no indiv. or popul. data; However: CPD Heat-List "präventiv von Beamten besucht" http://boingboing.net/2014/02/25/chicago-pds-big-data-using.html
Kaggle is a platform for data prediction competitions. Companies, organizations and researchers post their data and have it scrutinized by the world's best statisticians.
Kaggle is a platform for data prediction competitions. Companies, organizations and researchers post their data and have it scrutinized by the world's best statisticians.
TrueSkill™ Ranking System
TrueSkill™ Ranking System
The TrueSkill™ ranking system is a skill based ranking system for Xbox Live developed at Microsoft Research.
Our mission is to leverage the methods of machine learning and game theory for addressing relevant applications both in recreational games and in abstract decision games played in the real world.
The Prediction API provides pattern-matching and machine learning capabilities. Given a set of data examples to train against, you can create applications that can perform the following tasks
The real-time city is now real! The increasing deployment of sensors and hand-held electronics in recent years is allowing a new approach to the study of the built environment.
- http://senseable.mit.edu/obama/data_analysis.html
- http://senseable.mit.edu/realtimerome/
- http://senseable.mit.edu/trashtrack/
- http://www.mamartino.com/
- http://www.scientificamerican.com/article.cfm?id=ratti-smartest-cities-use-people-as-sensors Bilder:
- http://www.maind.supsi.ch/maindzine/wp-content/uploads/2008/10/fig-3.jpg
- http://flowingcity.com/wp-content/uploads/madonna-color-630x472.jpg
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Supercomputer predicts revolution:
http://www.bbc.co.uk/news/technology-14841018
The Prediction API enables access to Google's machine learning algorithms to analyze your historic data and predict likely future outcomes. Upload your data to Google Storage for Developers, then use the Prediction API to make real-time decisions in your applications. The Prediction API implements supervised learning algorithms as a RESTful web service to let you leverage patterns in your data, providing more relevant information to your users. Run your predictions on Google's infrastructure and scale effortlessly as your data grows in size and complexity.
Two-way latent grouping model for user preference prediction
Eerika Savia, Kai Puolamäki, Janne Sinkkonen and Samuel Kaski
In: UAI 2005, 26-29 July 2005, Edinburgh, Scotland.
P2P and the neural network in recognizing and predicting sociocultural, sociopolitical, and other transactional patterns...a self-aware society capable of more sophisticated and proactive pattern detection and recognition...
P2P and the neural network in recognizing and predicting sociocultural, sociopolitical, and other transactional patterns...a self-aware society capable of more sophisticated and proactive pattern detection and recognition...
J. Choi, A. Khlif, and E. Epure. Proceedings of the 1st Workshop on NLP for Music and Audio (NLP4MusA), page 23--27. Online, Association for Computational Linguistics, (2020)
J. Choi, A. Khlif, and E. Epure. Proceedings of the 1st Workshop on NLP for Music and Audio (NLP4MusA), page 23--27. Online, Association for Computational Linguistics, (2020)
L. Johannsen, S. Ramm, Y. Reckleben, and S. Doerfel. Informatik in der Land-, Forst- und Ernährungswirtschaft, volume 344 of Lecture Notes in Informatics, page 107-118. (2024)
M. Hoq, S. Chilla, M. Ranjbar, P. Brusilovsky, and B. Akram. Proceedings of the 32nd ACM International Conference on Information and Knowledge Management, page 783-792. ACM, (October 2023)
J. Weyn, D. Durran, and R. Caruana. Journal of Advances in Modeling Earth Systems, 11 (8):
2680--2693(2019)\_eprint: https://onlinelibrary.wiley.com/doi/pdf/10.1029/2019MS001705.
M. Cardia, M. Luca, and L. Pappalardo. Companion Proceedings of the Web Conference 2022, page 1251–1259. New York, NY, USA, Association for Computing Machinery, (2022)