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.
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.
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 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.
M. Züfle, and S. Kounev. Proceedings of the 15th Conference on Computer Science and Information Systems (FedCSIS): Data Mining Competition of the International Symposium on Advanced Artificial Intelligence in Applications, (September 2020)
M. Züfle, and S. Kounev. Proceedings of the 15th Conference on Computer Science and Information Systems (FedCSIS): Data Mining Competition of the International Symposium on Advanced Artificial Intelligence in Applications, (September 2020)
M. Züfle, and S. Kounev. Proceedings of the 15th Conference on Computer Science and Information Systems (FedCSIS): Data Mining Competition of the International Symposium on Advanced Artificial Intelligence in Applications, (September 2020)
Q. Noorshams, A. Rentschler, S. Kounev, and R. Reussner. Proceedings of the ACM/SPEC International Conference on Performance Engineering, page 339--342. New York, NY, USA, ACM, (2013)
Q. Noorshams, A. Rentschler, S. Kounev, and R. Reussner. Proceedings of the ACM/SPEC International Conference on Performance Engineering, page 339--342. New York, NY, USA, ACM, (2013)