In this paper, we propose adding discretized values of the continuous variables in the data as input when training the neural networks. Finding out whether the ...
In this paper, we propose adding discretized values of the continuous variables in the data as input when training the neural networks. Finding out whether the ...
In this paper, we propose adding discretized values of the continuous variables in the data as input when training the neural networks. Finding out whether the ...
Neural networks are widely used for classification as they achieve good predictive accuracy. When the class labels are determined by complex interactions.
Discrete variable generation for improved neural network classification. Author: Setiono, Rudy. Seret, Alex. Keywords: Science & Technology, Technology ...
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In this paper, a novel discrete learning process is offered to eliminate the inconsistency between the cost function and the classification objective function.
May 19, 2021 · I'm building a model (neural net) that would predict a quality score for images. Ground truth is given by a 4-level discrete variable (0%, ...
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Mar 19, 2024 · Using discrete latent variables for such data can lead to models that are both simpler and more effective. Control over Generative Processes: In ...
It consists of three components: a field-wise network consisting of one unique deep neural network for every column to capture the column-specific information, ...
This paper proposes a novel approach that combines theory- and data-driven choice models using Artificial Neural Networks (ANNs).