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Multiple Classifier Fusion in Probabilistic Neural Networks

https://doi.org/10.1007/s100440200020

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What this work says

Outgoing claims: the papers this work cites, builds on, or argues with.

inferredcites →https://doi.org/10.1007/3-540-45014-9_15openalex-snapshot

Combining Multiple Classifiers in Probabilistic Neural Networks

inferredcites →https://doi.org/10.1007/3-540-48219-9_17openalex-snapshot

Information Analysis of Multiple Classifier Fusion?

inferredcites →https://doi.org/10.1007/978-3-642-60187-3_15openalex-snapshot

A Sequential Modification of EM Algorithm

inferredcites →https://doi.org/10.1007/bf01238023openalex-snapshot

Combining classifiers: A theoretical framework

inferredcites →https://doi.org/10.1007/bfb0033301openalex-snapshot

On virtually binary nature of probabilistic neural networks

inferredcites →https://doi.org/10.1016/0031-3203(88)90068-4openalex-snapshot

Structural classification and relaxation matching of totally unconstrained handwritten zip-code numbers

inferredcites →https://doi.org/10.1016/0031-3203(94)00009-bopenalex-snapshot

Feature selection based on the approximation of class densities by finite mixtures of special type

inferredcites →https://doi.org/10.1016/0165-1684(84)90028-8openalex-snapshot

Subspace methods of pattern recognition

inferredcites →https://doi.org/10.1016/0893-6080(94)90099-xopenalex-snapshot

Combining the results of several neural network classifiers

inferredcites →https://doi.org/10.1016/0893-6080(95)00020-zopenalex-snapshot

Multilayer cluster neural network for totally unconstrained handwritten numeral recognition

inferredcites →https://doi.org/10.1109/21.105083openalex-snapshot

A generalized knowledge-based system for the recognition of unconstrained handwritten numerals

inferredcites →https://doi.org/10.1109/21.364825openalex-snapshot

Combining multiple neural networks by fuzzy integral for robust classification

inferredcites →https://doi.org/10.1109/34.481557openalex-snapshot

Divergence based feature selection for multimodal class densities

inferredcites →https://doi.org/10.1109/34.58871openalex-snapshot

Neural network ensembles

inferredcites →https://doi.org/10.1109/34.655648openalex-snapshot

Example-based learning for view-based human face detection

inferredcites →https://doi.org/10.1109/34.667881openalex-snapshot

On combining classifiers

inferredcites →https://doi.org/10.1109/5.58326openalex-snapshot

Networks for approximation and learning

inferredcites →https://doi.org/10.1109/72.317728openalex-snapshot

Maximum likelihood training of probabilistic neural networks

inferredcites →https://doi.org/10.1109/72.363487openalex-snapshot

Multiple network fusion using fuzzy logic

inferredcites →https://doi.org/10.1109/72.377990openalex-snapshot

Improving model accuracy using optimal linear combinations of trained neural networks

inferredcites →https://doi.org/10.1109/72.554190openalex-snapshot

Neural-network classifiers for recognizing totally unconstrained handwritten numerals

inferredcites →https://doi.org/10.1109/72.554192openalex-snapshot

Modeling the manifolds of images of handwritten digits

inferredcites →https://doi.org/10.1109/72.554201openalex-snapshot

Growing subspace pattern recognition methods and their neural-network models

inferredcites →https://doi.org/10.1109/icnn.1988.23887openalex-snapshot

Probabilistic neural networks for classification, mapping, or associative memory

inferredcites →https://doi.org/10.1109/icpr.1994.576989openalex-snapshot

A new method for generating statistical classifiers assuming linear mixtures of Gaussian densities

inferredcites →https://doi.org/10.1109/icpr.1996.547239openalex-snapshot

Maximum-likelihood design of layered neural networks

inferredcites →https://doi.org/10.1109/icpr.1996.547643openalex-snapshot

An efficient method to construct a radial basis function neural network classifier and its application to unconstrained handwritten digit recognition

inferredcites →https://doi.org/10.1111/j.2517-6161.1977.tb01600.xopenalex-snapshot

Maximum Likelihood from Incomplete Data Via the EM Algorithm

inferredcites →https://doi.org/10.1142/s0129065789000475openalex-snapshot

NEURAL NETWORKS, PRINCIPAL COMPONENTS, AND SUBSPACES

inferredcites →https://doi.org/10.1162/089976699300016728openalex-snapshot

Mixtures of Probabilistic Principal Component Analyzers

inferredcites →https://doi.org/10.2307/2289504openalex-snapshot

Statistical Analysis of Finite Mixture Distributions.

What points at this work

Incoming claims: where this work appears in other papers’ reference lists and arguments.

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Noisy Digit Classification with Multiple Specialist

inferred← citeshttps://doi.org/10.1007/11815921_70openalex-snapshot

EM Cluster Analysis for Categorical Data

inferred← citeshttps://doi.org/10.1007/11892755_29openalex-snapshot

Color Texture Segmentation by Decomposition of Gaussian Mixture Model

inferred← citeshttps://doi.org/10.1007/978-3-319-26393-9_22openalex-snapshot

Mixtures of Product Components Versus Mixtures of Dependence Trees

inferred← citeshttps://doi.org/10.1007/978-3-540-73499-4_18openalex-snapshot

Minimum Information Loss Cluster Analysis for Categorical Data

inferred← citeshttps://doi.org/10.1007/978-3-540-74690-4_14openalex-snapshot

Recurrent Bayesian Reasoning in Probabilistic Neural Networks

inferred← citeshttps://doi.org/10.1007/978-3-540-87559-8_6openalex-snapshot

Extraction of Binary Features by Probabilistic Neural Networks

inferred← citeshttps://doi.org/10.1016/j.neunet.2008.03.002openalex-snapshot

Iterative principles of recognition in probabilistic neural networks

inferred← citeshttps://doi.org/10.1016/j.patrec.2005.03.008openalex-snapshot

Probabilistic neural network playing and learning Tic–Tac–Toe

inferred← citeshttps://doi.org/10.1109/fuzz-ieee.2014.6891812openalex-snapshot

A new fuzzy approach for multi-source decision fusion

inferred← citeshttps://doi.org/10.1109/icarcv.2004.1468945openalex-snapshot

A robust doorplate recognition system

inferred← citeshttps://doi.org/10.1109/icpr.2006.181openalex-snapshot

A Subspace Approach to Texture Modelling by Using Gaussian Mixtures

inferred← citeshttps://doi.org/10.1109/icpr.2008.4761669openalex-snapshot

Structural poisson mixtures for classification of documents

inferred← citeshttps://doi.org/10.1109/isp.2003.1275832openalex-snapshot

Parameter estimation using a committee of local expert RBF networks

inferred← citeshttps://doi.org/10.1109/sysose.2007.4304292openalex-snapshot

Multi-Class Classification Averaging Fusion for Detecting Steganography

inferred← citeshttps://doi.org/10.1109/tnn.2008.2005301openalex-snapshot

Local Classifier Weighting by Quadratic Programming

inferred← citeshttps://doi.org/10.1109/tpami.2008.155openalex-snapshot

A Hybrid Feature Extraction Selection Approach for High-Dimensional Non-Gaussian Data Clustering

inferred← citeshttps://doi.org/10.1109/tsmcb.2006.883873openalex-snapshot

An Ensemble-Based Incremental Learning Approach to Data Fusion

inferred← citeshttps://doi.org/10.1117/1.2345056openalex-snapshot

Hopfield-multilayer-perceptron serial combination for accurate degraded printed character recognition

inferred← citeshttps://doi.org/10.1142/s0218001417500288openalex-snapshot

Approximation of Unknown Multivariate Probability Distributions by Using Mixtures of Product Components: A Tutorial

inferred← citeshttps://doi.org/10.5220/0005077500650075openalex-snapshot

Pattern Recognition by Probabilistic Neural Networks - Mixtures of Product Components versus Mixtures of Dependence Trees

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