Kai Heinrich, Scientific Assistant, TU Dresden, Germany Radhakrishnan Delhibabu, Associate Professor, Kazan Federal University, Russia Keywords Game Theory, Web Mining, Mechanism Design, Behavioral Science, Machine Learning, Business Intelligence, Data Mining, Experimental Economics, Complex Networks, Economet...
Moreover, the challenges that come with urban sensor data for the design and application of learning algorithms are also not coped with sufficiently yet. Consequently, algorithms that are suited to the special needs this kind of data imposes such as missing values, unreliable measurements, missing ...
Kai Heinrich, Scientific Assistant, TU Dresden, Germany Radhakrishnan Delhibabu, Associate Professor, Kazan Federal University, Russia Keywords Game Theory, Web Mining, Mechanism Design, Behavioral Science, Machine Learning, Business Intelligence, Data Mining, Experimental Economics, Complex Networks, Economet...
rainfall;rainfall prediction;machine learning;data fusion;fuzzy system;smart cities;big data;hydrological model;information systems;precipitation 1. Introduction Knowledge extraction from time series data has become a widely explored research area [1,2]. Data which are collected with time stamps in a ...
In Encyclopedia of Machine Learning and Data Mining; Sammut, C., Webb, G.I., Eds.; Springer US: Boston, MA, USA, 2016; p. 781. [Google Scholar] [CrossRef] Schönberger, J.L.; Frahm, J.M. Structure-from-Motion Revisited. In Proceedings of the 2016 IEEE Conference on Computer ...
One of the most used unsupervised learning algorithms is clustering. In clustering [54], the input data structure is divided into different groups, and hence the name clustering, in a way that items in the same group share similarities more than items in other groups. 3.2. Adoption in the ...
The Machine Learning Loss Reserving module (Taylor 2019; Taylor and McGuire 2016) allows users to choose from various machine learning methods for loss reserving. AutoReserve provides its users with free and easy access to both traditional and machine learning loss reserving techniques. Currently, ...
Automated machine learning (AutoML), which aims to facilitate the design and optimization of machine-learning models with reduced human effort and expertise, is a research field with significant potential to drive the development of artificial intelligence in science and industry. However, AutoML also ...
Deep learning (DL) [9] provides a powerful solution to above weaknesses. Unlike traditional models that are mostly based on handcrafted features, Deep neural network (DNN) can operate directly on raw data and learn features from a low level to a higher level to represent the distributed ...