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Search and classification of web shops

By Aron Birsa

Abstract

The aim of the thesis was to develop a tool for automatic classification of online stores depending on the type of products they offer. Websites are classified into seven predefined categories: antiques and collectibles, cloth- ing, consumer electronics, furniture, home and garden, jewelry and office products. The main problem was getting relevant data to build a learning and test data set and classifying web sites. The following machine learning methods were used: naive Bayesian classifier, k-nearest neighbors algorithm, random forests, neural networks and support vector machine. The most promising result were obtained using the support vector machine classifier

Topics: Computer and Information Science
Year: 2017
OAI identifier: oai:generic.eprints.org:3757/core382
Provided by: ePrints.FRI

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