Artificial neural networks (ANN), especially, multilayer perceptrons (MLP) have been widely used in pattern recognition and classification. Nevertheless, how to incorporate a priori knowledge in the design of ANNs is still an open problem. The paper tries to give some insight on this topic emphasizing weight initialization from three perspectives. Theoretical analyses and simulations are offered for validatio
While many implementations of Bayesian neural networks use large, complex hierarchical priors, in mu...
A method has been proposed for weight initialization in back-propagation feed-forward networks. Trai...
The paper describes what to consider when constructing multi-classifier systems (MCS), what is perce...
Artificial neural networks (ANN), esp. multilayer perceptrons (MLP) have been widely used in pattern...
A new method of initializing the weights in deep neural networks is proposed. The method follows two...
Proper initialization is one of the most important prerequisites for fast convergence of feed-forwar...
Abstracf- Proper initialization of neural networks is critical for a successful training of its weig...
The paper is devoted to the comparison of different approaches to initialization of neural network w...
The main idea of a priori machine learning is to apply a machine learning method on a machine learni...
In this paper, a novel data-driven method for weight initialization of Multilayer Perceptrons andCon...
This study high lights on the subject of weight initialization in back-propagation feed-forward netw...
This thesis explores the relationship between two classification models: decision trees and multilay...
A good weight initialization is crucial to accelerate the convergence of the weights in a neural net...
Abstract. The main idea of a priori machine learning is to apply a machine learning method on a mach...
In this paper, we present a new learning method using prior information for three-layer neural netwo...
While many implementations of Bayesian neural networks use large, complex hierarchical priors, in mu...
A method has been proposed for weight initialization in back-propagation feed-forward networks. Trai...
The paper describes what to consider when constructing multi-classifier systems (MCS), what is perce...
Artificial neural networks (ANN), esp. multilayer perceptrons (MLP) have been widely used in pattern...
A new method of initializing the weights in deep neural networks is proposed. The method follows two...
Proper initialization is one of the most important prerequisites for fast convergence of feed-forwar...
Abstracf- Proper initialization of neural networks is critical for a successful training of its weig...
The paper is devoted to the comparison of different approaches to initialization of neural network w...
The main idea of a priori machine learning is to apply a machine learning method on a machine learni...
In this paper, a novel data-driven method for weight initialization of Multilayer Perceptrons andCon...
This study high lights on the subject of weight initialization in back-propagation feed-forward netw...
This thesis explores the relationship between two classification models: decision trees and multilay...
A good weight initialization is crucial to accelerate the convergence of the weights in a neural net...
Abstract. The main idea of a priori machine learning is to apply a machine learning method on a mach...
In this paper, we present a new learning method using prior information for three-layer neural netwo...
While many implementations of Bayesian neural networks use large, complex hierarchical priors, in mu...
A method has been proposed for weight initialization in back-propagation feed-forward networks. Trai...
The paper describes what to consider when constructing multi-classifier systems (MCS), what is perce...