Estimation of the probability density function and aposterioriprobability by neural networks, and applications to vowel recognition

1994 ◽  
Vol 25 (6) ◽  
pp. 32-40
Author(s):  
Seiichi Nakagawa ◽  
Yoshiyuki Ono
Keyword(s):  
Neural Networks ◽  
Probability Density Function ◽  
Probability Density ◽  
Density Function ◽  
Vowel Recognition

Rate of Convergence in Density Estimation Using Neural Networks

1996 ◽  
Vol 8 (5) ◽  
pp. 1107-1122 ◽  
Author(s):  
Dharmendra S. Modha ◽  
Elias Masry
Keyword(s):  
Neural Networks ◽  
Probability Density Function ◽  
Probability Density ◽  
Rate Of Convergence ◽  
Density Function ◽  
Density Estimation ◽  
Exponential Family ◽  
Density Estimator ◽  
Estimation Scheme ◽  
Hidden Layer

Given N i.i.d. observations {Xi}Ni=1 taking values in a compact subset of Rd, such that p* denotes their common probability density function, we estimate p* from an exponential family of densities based on single hidden layer sigmoidal networks using a certain minimum complexity density estimation scheme. Assuming that p* possesses a certain exponential representation, we establish a rate of convergence, independent of the dimension d, for the expected Hellinger distance between the proposed minimum complexity density estimator and the true underlying density p*.

scholarly journals A probability density function generator based on neural networks

2020 ◽  
Vol 541 ◽  
pp. 123344 ◽  
Author(s):  
Chi-Hua Chen ◽  
Fangying Song ◽  
Feng-Jang Hwang ◽  
Ling Wu
Keyword(s):  
Neural Networks ◽  
Probability Density Function ◽  
Probability Density ◽  
Density Function ◽  
Function Generator

scholarly journals A Model of Extracting Building from High Resolution Remote Sensing Image Based on Bayesian Convolutional Neural Networks

2018 ◽  
Author(s):  
Xiaoxia Yang ◽  
Chengming Zhang ◽  
Shuai Gao ◽  
Fan Yu ◽  
Dejuan Song ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Neural Networks ◽  
High Resolution ◽  
Probability Density Function ◽  
Probability Density ◽  
Density Function ◽  
Feature Vector ◽  
Remote Sensing Image ◽  
Conditional Probability Density ◽  
Conditional Probability Density Function

When extract building from high resolution remote sensing image with meter/sub-meter accuracy, the shade of trees and interference of roads are the main factors of reducing the extraction accuracy. Proposed a Bayesian Convolutional Neural Networks(BCNET) model base on standard fully convolutional networks(FCN) to solve these problems. First take building with no shade or artificial removal of shade as Sample-A, woodland as Sample-B, road as Sample-C. Set up 3 sample libraries. Learn these sample libraries respectively, get their own set of feature vector; Mixture Gauss model these feature vector set, evaluate the conditional probability density function of mixture of noise object and roofs; Improve the standard FCN from the 2 aspect:(1) Introduce atrous convolution. (2) Take conditional probability density function as the activation function of the last convolution. Carry out experiment using unmanned aerial vehicle(UVA) image, the results show that BCNET model can effectively eliminate the influence of trees and roads, the building extraction accuracy can reach 97%.

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