Comparing BP and ART II neural network classifiers for facility location
Computers and Industrial Engineering
A class of instantaneously trained neural networks
Information Sciences—Applications: An International Journal
Strategic Selection and Replication of Movies by Trend-Calibrated Movie-Demand Model
MSE '00 Proceedings of the 2000 International Conference on Microelectronic Systems Education
MOVIE: an incremental maintenance system for materialized object views
Data & Knowledge Engineering
Forecasting the volatility of stock price index
Expert Systems with Applications: An International Journal
Movie forecast Guru: A Web-based DSS for Hollywood managers
Decision Support Systems
The use of data mining and neural networks for forecasting stock market returns
Expert Systems with Applications: An International Journal
A comparison of supervised and unsupervised neural networks in predicting bankruptcy of Korean firms
Expert Systems with Applications: An International Journal
Predicting box-office success of motion pictures with neural networks
Expert Systems with Applications: An International Journal
Paper: Neural network based classification of single-trial EEG data
Artificial Intelligence in Medicine
A comparative study of neural network models
Mathematical and Computer Modelling: An International Journal
Early warning of enterprise decline in a life cycle using neural networks and rough set theory
Expert Systems with Applications: An International Journal
Pre-release box-office success prediction for motion pictures
MLDM'13 Proceedings of the 9th international conference on Machine Learning and Data Mining in Pattern Recognition
Hi-index | 12.05 |
Forecasting box office revenue of a movie before its theatrical release is a difficult and challenging problem. In this study, a multi-layer BP neural network (MLBP) with multi-input and multi-output is employed to build the prediction model. All the movies are divided into six categories ranged from ''blob'' to ''bomb'' according to their box office incomes, and the purpose is to predict a film into the right class. The selections of the input variables are based on market survey and their weight values are determined by using statistical method. As to the design of the neural network structure, theoretical guidance and plentiful experiments are combined to optimize the hidden layers' parameters which include the number of hidden layers and their node numbers. Then a classifier with dynamic thresholds is used to standardize the output for the first time, and it improves the robustness of the model to a high level. Finally, a 6-fold cross-validation experiment methodology is used to measure the performance of the prediction model. The comparison results with the MLP method show that the MLBP prediction model achieves more satisfactory results, and it is more reliable and effective to solve the problem.