Analysis of learning algorithms for the similarity neural network
Tutor / Supervisor
Student
Mallela, Pravallika
Document type
Bachelor thesis
Date
2020
rights
Open Access
Publisher
Universitat Politècnica de Catalunya
UPCommons
Abstract
A similarity neural network is a two-layer neural network based on similarity measures: the first layer computes the similarity between the input data and a set of prototypes, and the second layer gathers these results and predicts the output. The goal of the project is to analyze fast training algorithms and compare them with more traditional learning algorithms, such as a standard two-layer MLP network. Along with the usage of similarity measures, this research also aims to develop a model that can handle complex, heterogeneous, imprecise, missing, and complex data without changing their original form.
Entitat col·laboradora
SASTRA Deemed University
