Multi-class pattern classification using neural networks
Pattern Recognition
Neural Computing and Applications
IEEE Transactions on Knowledge and Data Engineering
A multitask learning model for online pattern recognition
IEEE Transactions on Neural Networks
A multiscale scheme for approximating the quantron's discriminating function
IEEE Transactions on Neural Networks
Parallel-series perceptrons for the simultaneous determination of odor classes and concentrations
ICANN'07 Proceedings of the 17th international conference on Artificial neural networks
Estimating gas concentration using a microcantilever-based electronic nose
Digital Signal Processing
A very fast neural learning for classification using only new incoming datum
IEEE Transactions on Neural Networks
IEEE Transactions on Neural Networks
Local-Learning-Based Feature Selection for High-Dimensional Data Analysis
IEEE Transactions on Pattern Analysis and Machine Intelligence
Neural network learning without backpropagation
IEEE Transactions on Neural Networks
Periodic activation function and a modified learning algorithm for the multivalued neuron
IEEE Transactions on Neural Networks
Node perturbation learning without noiseless baseline
Neural Networks
Anytime learning of anycost classifiers
Machine Learning
Classifier chains for multi-label classification
Machine Learning
A balanced neural tree for pattern classification
Neural Networks
An Optimization Methodology for Neural Network Weights and Architectures
IEEE Transactions on Neural Networks
Backpropagation Algorithms for a Broad Class of Dynamic Networks
IEEE Transactions on Neural Networks
Incremental Hierarchical Discriminant Regression
IEEE Transactions on Neural Networks
Reduced Pattern Training Based on Task Decomposition Using Pattern Distributor
IEEE Transactions on Neural Networks
IEEE Transactions on Neural Networks
Multilayer Perceptrons: Approximation Order and Necessary Number of Hidden Units
IEEE Transactions on Neural Networks
Learning Ensembles of Neural Networks by Means of a Bayesian Artificial Immune System
IEEE Transactions on Neural Networks
Uniformly Stable Backpropagation Algorithm to Train a Feedforward Neural Network
IEEE Transactions on Neural Networks
Efficient classification for multiclass problems using modular neural networks
IEEE Transactions on Neural Networks
A New Formulation for Feedforward Neural Networks
IEEE Transactions on Neural Networks
Adaptive Evolutionary Artificial Neural Networks for Pattern Classification
IEEE Transactions on Neural Networks
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This paper studies several types and arrangements of perceptron modules to discriminate and quantify multiple odors with an electronic nose. We evaluate the following types of multilayer perceptron. (A) A single multi-output (SMO) perceptron both for discrimination and for quantification. (B) An SMO perceptron for discrimination followed by multiple multi-output (MMO) perceptrons for quantification. (C) An SMO perceptron for discrimination followed by multiple single-output (MSO) perceptrons for quantification. (D) MSO perceptrons for discrimination followed by MSO perceptrons for quantification, called the MSO-MSO perceptron model, under the following conditions: (D1) using a simple one-against-all (OAA) decomposition method; (D2) adopting a simple OAA decomposition method and virtual balance step; and (D3) employing a local OAA decomposition method, virtual balance step and local generalization strategy all together. The experimental results for 12 kinds of volatile organic compounds at 85 concentration levels in the training set and 155 concentration levels in the test set show that the MSO-MSO perceptron model with the D3 learning procedure is the most effective of those tested for discrimination and quantification of many kinds of odors.