Showing 1 - 10 of 12
Singular value decomposition (SVD) is a way to decompose a matrix into some successive approximation. This … then approximate it by SVD. Obtained matrix is very useful for creating new feature space. We prove our approach by …
Persistent link: https://www.econbiz.de/10011207173
We investigate the issue of predicting new customers as profitable based on information about existing customers in a business-to-business environment. In particular, we show how latent semantic concepts from textual information of existing customers’ websites can be used to uncover...
Persistent link: https://www.econbiz.de/10009392903
The detection of chaotic behaviors in commodities, stock markets and weather data is usually complicated by large noise perturbation inherent to the underlying system. It is well known, that predictions, from pure deterministic chaotic systems can be accurate mainly in the short term. Thus, it...
Persistent link: https://www.econbiz.de/10010750796
In recent years, governmental and industrial espionage becomes an increased problem for governments and corporations. Especially information about current technology development and research activities are interesting targets for espionage. Thus, we introduce a new and automated methodology that...
Persistent link: https://www.econbiz.de/10011083097
the largest number of relevant weak signals represented by singular value decomposition (SVD) dimensions. A case study …
Persistent link: https://www.econbiz.de/10011083143
Many national and international governments establish organizations for applied science research funding. For this, several organizations have defined procedures for identifying relevant projects that based on prioritized technologies. Even for applied science research projects, which combine...
Persistent link: https://www.econbiz.de/10011083152
Driven by increased complexity of dynamical systems, the solution of system of differential equations through numerical simulation in optimization problems has become computationally expensive. This paper provides a smart data driven mechanism to construct low dimensional surrogate models. These...
Persistent link: https://www.econbiz.de/10012433273
The detection of chaotic behaviors in commodities, stock markets and weather data is usually complicated by large noise perturbation inherent to the underlying system. It is well known, that predictions, from pure deterministic chaotic systems can be accurate mainly in the short term. Thus, it...
Persistent link: https://www.econbiz.de/10005797753
Many economic and financial time series exhibit time-varying volatility. GARCH models are tools for forecasting and analyzing the dynamics of this volatility. The co-movements in financial markets and financial assets around the globe have recently become the main area of interest of financial...
Persistent link: https://www.econbiz.de/10011459899
Persistent link: https://www.econbiz.de/10011942224