Perceptron: Learning, generalization, model selection, fault tolerance, and role in the deep learning era
The single-layer perceptron, introduced by Rosenblatt in 1958, is one of the earliest and
simplest neural network models. However, it is incapable of classifying linearly inseparable …
simplest neural network models. However, it is incapable of classifying linearly inseparable …
Sparsity in deep learning: Pruning and growth for efficient inference and training in neural networks
The growing energy and performance costs of deep learning have driven the community to
reduce the size of neural networks by selectively pruning components. Similarly to their …
reduce the size of neural networks by selectively pruning components. Similarly to their …
Identifying the most significant input parameters for predicting global solar radiation using an ANFIS selection procedure
K Mohammadi, S Shamshirband, A Kamsin… - … and Sustainable Energy …, 2016 - Elsevier
There are several variables that influence the global solar radiation (GSR) prediction; thus,
determining the most significant parameters is an important task to achieve accurate …
determining the most significant parameters is an important task to achieve accurate …
[图书][B] Computational intelligence: an introduction
AP Engelbrecht - 2007 - books.google.com
Computational Intelligence: An Introduction, Second Edition offers an in-depth exploration
into the adaptive mechanisms that enable intelligent behaviour in complex and changing …
into the adaptive mechanisms that enable intelligent behaviour in complex and changing …
Neural networks for classification: a survey
GP Zhang - IEEE Transactions on Systems, Man, and …, 2000 - ieeexplore.ieee.org
Classification is one of the most active research and application areas of neural networks.
The literature is vast and growing. This paper summarizes some of the most important …
The literature is vast and growing. This paper summarizes some of the most important …
[图书][B] Neural networks for applied sciences and engineering: from fundamentals to complex pattern recognition
S Samarasinghe - 2016 - taylorfrancis.com
In response to the exponentially increasing need to analyze vast amounts of data, Neural
Networks for Applied Sciences and Engineering: From Fundamentals to Complex Pattern …
Networks for Applied Sciences and Engineering: From Fundamentals to Complex Pattern …
A neural network approach using multi-scale textural metrics from very high-resolution panchromatic imagery for urban land-use classification
F Pacifici, M Chini, WJ Emery - Remote Sensing of Environment, 2009 - Elsevier
The successful launch of panchromatic WorldView-1 and the planned launch of WorldView-
2 will make a major contribution towards the advancement of the commercial remote …
2 will make a major contribution towards the advancement of the commercial remote …
Feature selection with neural networks
A Verikas, M Bacauskiene - Pattern recognition letters, 2002 - Elsevier
We present a neural network based approach for identifying salient features for classification
in feedforward neural networks. Our approach involves neural network training with an …
in feedforward neural networks. Our approach involves neural network training with an …
A new pruning heuristic based on variance analysis of sensitivity information
AP Engelbrecht - IEEE transactions on Neural Networks, 2001 - ieeexplore.ieee.org
Architecture selection is a very important aspect in the design of neural networks (NNs) to
optimally tune performance and computational complexity. Sensitivity analysis has been …
optimally tune performance and computational complexity. Sensitivity analysis has been …