Showing 1 - 7 of 7
Ensuring that organizational IT is in alignment with and provides support for an organization's business strategy is critical to business success. Despite this, business strategy and strategic alignment issues are all but ignored in the requirements engineering research literature. We present...
Persistent link: https://www.econbiz.de/10009429736
As a means of contributing to the achievement of business advantage for companies engaging in e-business, we propose a requirements engineering framework that incorporates a business strategy dimension. We employ Jackson’s Problem Frames approach, goal modeling, and business process modeling...
Persistent link: https://www.econbiz.de/10009429737
As a means of contributing to the achievement of business advantage for companies engaging in ebusiness, we propose a requirements engineering framework that incorporates a business strategy dimension. We employ Jackson’s Problem Frames approach, goal modeling, and business process modeling...
Persistent link: https://www.econbiz.de/10009429749
In this paper a classification framework for incomplete data, based on electrostatic field model is proposed. An original approach to exploiting incomplete training data with missing features, involving extensive use of electrostatic charge analogy, has been used. The framework supports a hybrid...
Persistent link: https://www.econbiz.de/10009429779
Estimation of the generalization ability of a predictive model is an important issue, as it indicates expected performance on previously unseen data and is also used for model selection. Currently used generalization error estimation procedures like cross–validation (CV) or bootstrap are...
Persistent link: https://www.econbiz.de/10009429791
Estimation of the generalization ability of a predictive model is an important issue, as it indicates expected performance on previously unseen data and is also used for model selection. Currently used generalization error estimation procedures like cross–validation (CV) or bootstrap are...
Persistent link: https://www.econbiz.de/10009429864
In this paper a classification framework for incomplete data, based on electrostatic field model is proposed. An original approach to exploiting incomplete training data with missing features, involving extensive use of electrostatic charge analogy, has been used. The framework supports a hybrid...
Persistent link: https://www.econbiz.de/10009429890