Learnability and the Vapnik-Chervonenkis dimension
Journal of the ACM (JACM)
What size net gives valid generalization?
Neural Computation
Advances in neural information processing systems 2
Generalization by weight-elimination with application to forecasting
NIPS-3 Proceedings of the 1990 conference on Advances in neural information processing systems 3
Practical neural network recipes in C++
Practical neural network recipes in C++
Constant depth circuits, Fourier transform, and learnability
Journal of the ACM (JACM)
Neural network constructive algorithms: trading generalization for learning efficiency?
Circuits, Systems, and Signal Processing - Special issue: networks for neural processing
On Optimal Depth Threshold Circuits for Multiplication andRelated Problems
SIAM Journal on Discrete Mathematics
Approximation and Estimation Bounds for Artificial Neural Networks
Machine Learning - Special issue on computational learning theory
A classification approach using multi-layered neural networks
Decision Support Systems - Special issue on neural networks for decision support
Neural Networks: A Comprehensive Foundation
Neural Networks: A Comprehensive Foundation
On the Power of Networks of Majority Functions
IWANN '91 Proceedings of the International Workshop on Artificial Neural Networks
Repeated Measures Multiple Comparison Procedures Applied to Model Selection in Neural Networks
IWANN '01 Proceedings of the 6th International Work-Conference on Artificial and Natural Neural Networks: Bio-inspired Applications of Connectionism-Part II
Bounds on the number of hidden neurons in three-layer binary
Neural Networks
Generalization and Selection of Examples in Feedforward Neural Networks
Neural Computation
Advances in Neural Networks - ISNN 2006: Third International Symposium on Neural Networks, ISNN 2006, Chengdu, China, May 28 - June 1, 2006, Proceedings, Part I (Lecture Notes in Computer Science)
Neural Computing and Applications
Data Mining: Practical Machine Learning Tools and Techniques, Second Edition (Morgan Kaufmann Series in Data Management Systems)
Statistical Comparisons of Classifiers over Multiple Data Sets
The Journal of Machine Learning Research
Optimizing number of hidden neurons in neural networks
AIAP'07 Proceedings of the 25th conference on Proceedings of the 25th IASTED International Multi-Conference: artificial intelligence and applications
Generalization properties of modular networks: implementing the parity function
IEEE Transactions on Neural Networks
IEEE Transactions on Neural Networks
Constructive neural networks to predict breast cancer outcome by using gene expression profiles
IEA/AIE'10 Proceedings of the 23rd international conference on Industrial engineering and other applications of applied intelligent systems - Volume Part I
Extension of the generalization complexity measure to real valued input data sets
ISNN'10 Proceedings of the 7th international conference on Advances in Neural Networks - Volume Part I
Data discretization using the extreme learning machine neural network
ICONIP'12 Proceedings of the 19th international conference on Neural Information Processing - Volume Part IV
Committee C-mantec: a probabilistic constructive neural network
IWANN'13 Proceedings of the 12th international conference on Artificial Neural Networks: advances in computational intelligence - Volume Part I
| Hi-index | 0.00 |
This work analyzes the problem of selecting an adequate neural network architecture for a given function, comparing existing approaches and introducing a new one based on the use of the complexity of the function under analysis. Numerical simulations using a large set of Boolean functions are carded out and a comparative analysis of the results is done according to the architectures that the different techniques suggest and based on the generalization ability obtained in each case. The results show that a procedure that utilizes the complexity of the function can help to achieve almost optimal results despite the fact that some variability exists for the generalization ability of similar complexity classes of functions.