An Assessment of Qualitative Performance of Machine Learning Architectures: Modular Feedback Networks
A framework for the assessment of qualitative performance of machine learning architectures is proposed. For generality, the analysis is provided for the modular nonlinear pipelined recurrent neural network (PRNN) architecture. This is supported by a sensitivity analysis, which is achieved based upon the prediction performance with respect to changes in the nature of the processed signal and by utilizing the recently introduced delay vector variance (DVV) method for phase space signal characterization. Comprehensive simulations combining the quantitative and qualitative analysis on both linear and nonlinear signals suggest that better quantitative prediction performance may need to be traded in order to preserve the nature of the processed signal, especially where the signal nature is of primary importance (biomedical applications).
US Government organizations are creating value for their citizens and businesses by improving their public service delivery through good websites. Our study examined leadership and IT quality, and their effect on positive delivery outcomes in an e-government environment. We first developed a theoretically based model using elements of the model developed for the Malcolm Baldrige National Quality Award (MBNQA) and DeLone and McLean's IS Success model. To test this model, we conducted a field survey at a municipal city government. The results supported our hypothesis that the MBNQA leadership triad (leadership, strategic planning, and customer/market focus) had a positive impact on the IT quality triad (information, system, and service quality). We also found that both leadership and IT quality increased the benefits.