Fuzzy learning vector quantization neural network and its application for artificial odor recognition system

2000 ◽  
Author(s):  
Benyamin Kusumoputro ◽  
Hary Budiarto ◽  
Wisnu Jatmiko
Keyword(s):  
Neural Network ◽  
Vector Quantization ◽  
Recognition System ◽  
Learning Vector Quantization ◽  
Odor Recognition ◽  
Fuzzy Learning

scholarly journals Clustering and Classification in Option Pricing

2011 ◽  
Vol 3 (2) ◽  
pp. 109-128
Author(s):  
Nikola Gradojevic ◽  
Dragan Kukolj ◽  
Ramazan Gencay
Keyword(s):  
Neural Network ◽  
Option Pricing ◽  
Vector Quantization ◽  
Learning Vector Quantization ◽  
Pricing Models ◽  
Call Options ◽  
Modular Neural Network ◽  
Clustering And Classification ◽  
Fuzzy Learning ◽  
Non Parametric

This paper reviews the recent option pricing literature and investigates how clustering and classification can assist option pricing models. Specifically, we consider non-parametric modular neural network (MNN) models to price the S&P-500 European call options. The focus is on decomposing and classifying options data into a number of sub-models across moneyness and maturity ranges that are processed individually. The fuzzy learning vector quantization (FLVQ) algorithm we propose generates decision regions (i.e., option classes) divided by ‘intelligent’ classification boundaries. Such an approach improves generalization properties of the MNN model and thereby increases its pricing accuracy.

scholarly journals Diagnosa Penyakit Demam Berdarah Dengue (DBD) menggunakan Metode Learning Vector Quantization (LVQ)

2020 ◽  
Vol 4 (3) ◽  
pp. 56
Author(s):  
Firman Tawakal ◽  
Ahmedika Azkiya
Keyword(s):  
Neural Network ◽  
Typhoid Fever ◽  
Vector Quantization ◽  
Dengue Hemorrhagic Fever ◽  
Hemorrhagic Fever ◽  
Learning Vector Quantization ◽  
Mean Square ◽  
Northern Australia ◽  
Neural Network Learning ◽  
Network Learning

Dengue Hemorrhagic Fever is a disease that is carried and transmitted through the mosquito Aedes aegypti and Aedes albopictus which is commonly found in tropical and subtropical regions such as in Indonesia to Northern Australia. in 2013 there are 2.35 million reported cases, which is 37,687 case is heavy cases of DHF. DHF’s symthoms have a similarity with typhoid fever, it often occur wrong handling. Therefore we need a system that is able to diagnose the disease suffered by patients, so that they can recognize whether the patient has DHF or Typhoid. The system will be built using Neural Network Learning Vector Quantization (LVQ) based on the best training results. This research is to diagnose Dengue Hemorrhagic Fever using LVQ with input parameters are hemoglobin, leukocytes, platelets, and heritrocytes. Based on result, the best accuracy is 97,14% with Mean Square Error (MSE) is 0.028571 with 84 train data and 36 test data. Conclution from the research is LVQ method can diagnose DHF Keywords: Dengue Hemorrhagic Fever; Learning Vector Quantization; classification; Neural Network;

scholarly journals Multipose Face Recognition-Based Combined Adaptive Deep Learning Vector Quantization

2020 ◽  
Vol 2020 ◽  
pp. 1-11
Author(s):  
Shahenda Sarhan ◽  
Aida A. Nasr ◽  
Mahmoud Y. Shams
Keyword(s):  
Neural Networks ◽  
Deep Learning ◽  
Face Recognition ◽  
Vector Quantization ◽  
Confusion Matrix ◽  
Recognition System ◽  
Learning Vector Quantization ◽  
Majority Voting ◽  
Support Vector ◽  
Reliability And Robustness

Multipose face recognition system is one of the recent challenges faced by the researchers interested in security applications. Different researches have been introduced discussing the accuracy improvement of multipose face recognition through enhancing the face detector as Viola-Jones, Real Adaboost, and Cascade Object Detector while others concentrated on the recognition systems as support vector machine and deep convolution neural networks. In this paper, a combined adaptive deep learning vector quantization (CADLVQ) classifier is proposed. The proposed classifier has boosted the weakness of the adaptive deep learning vector quantization classifiers through using the majority voting algorithm with the speeded up robust feature extractor. Experimental results indicate that, the proposed classifier provided promising results in terms of sensitivity, specificity, precision, and accuracy compared to recent approaches in deep learning, statistical, and classical neural networks. Finally, the comparison is empirically performed using confusion matrix to ensure the reliability and robustness of the proposed system compared to the state-of art.

Sign in / Sign up

Export Citation Format

Share Document