Media mi elements affecting brand equity A study of the Indian passenger car market分析和总结分析和总结.docx

state-of-the-art machine learning approach known as support vector regression (SVR) is introduced to develop a model that predicts consumers? affective responses (CARs) for product form design. First, pairwise adjectives were used to describe the CARs toward product samples. Second, the product form features (PFFs) were examined systematically and then stored them either as continuous or discrete attributes. The adjective evaluation data of consumers were gathered from questionnaires. Finally, prediction models based on different adjectives were constructed using SVR, which trained a series of PFFs and the average CAR rating of all the respondents. The real-coded genetic algorithm (RCGA) was used to determine the optimal training parameters of SVR. The predictive performance of the SVR with RCGA (SVR–RCGA) is compared to that of SVR with 5-fold cross-validation (SVR–5FCV) and a back-propagation neural network (BPNN) with 5-fold cross-validation (BPNN–5FCV). The experimental results using the data sets on mobile phones and electronic scooters show that SVR performs better than BPNN. Moreover, the RCGA for optimizing training parameters for SVR is more convenient for practical usage in product form design than the timeconsuming CV.  Purchase $ 37.95 Article Outline Introduction Theoretical backgrounds Support vector regression Prediction model of consumer?s affective responses for product form design Describing the affective responses of consumer with pairwise adjectives Representing sparse and mixed product form features Questionnaire investigation for adjective evaluation Constructing the support vector regression prediction model Optimizing training parameters of SVR using real-coded genetic algorithm Constructing the back-propagation neural network prediction model Experimental results Analysis of the optimization process using RCGA Comparison Comparison of predictive performance for different kernel functions Comparison of predictive performance for SVR–RCGA, S