The results show that on some datasets the use of unlabeled examples can lead to an improvement in classification performance over that of conventional ...
A GA-based neural network weight optimization technique for semi-supervised classifier learning ; End date. 2003-12-17 ; ISBN-13. 9781586033941 ; ISBN-10.
We propose a GA-based feature selection and parameter optimization algorithm for SHTM. •. The proposed GA is coded according to the tensor rank-one ...
Nov 28, 2017 · I prepared a complete tutorial about optimizing artificial neural networks using genetic algorithm with Python implementation.
Missing: Semi- Supervised
This paper employs a standard genetic algorithm (GA) to train the weights of a 4-5x5 filter CNN in order to pass through the local minima. This technique ...
This study combines the new weighted kernel with SKELM and proposes a semi-supervised extreme learning machine algorithm based on the weighted kernel, SELMWK.
Jan 15, 2014 · Genetic Algorithm is an evolutionary algorithm for solving both constrained and unconstrained optimization with mimicking biological evolution ...
Jul 18, 2019 · In a neural net- work, the hidden layer neural nodes are summed from the weights of the upper nodes, and the results are transmitted to the next ...
May 9, 2014 · The best method however of avoiding local minima in neural networks is to use a Gaussian Process model (or a Radial Basis Function neural ...
The basic theory of deep learning is based on an artificial neural network. Because of the repeated superposition of these non-linear functions the neural ...