izpis_h1_title_alt

Iskanje in razvrščanje spletnih trgovin
ID BIRSA, ARON (Author), ID Robnik Šikonja, Marko (Mentor) More about this mentor... This link opens in a new window

.pdfPDF - Presentation file, Download (737,37 KB)
MD5: 3B7E237247E23027DC89EDA53763B1A1

Abstract
Cilj diplomske naloge je razvoj orodja, ki omogoča avtomatsko zaznavanje spletnih trgovin glede na tip izdelkov, ki jih ponuja. Spletne strani smo klasificirali v sedem vnaprej določenih kategorij: starine in zbirke, oblačila, zabavna elektronika, pohištvo, dom in vrt, nakit in pisarniški izdelke. Glavni problem je bil pridobivanje ustreznih podatkov za izgradnjo učne in testne množice ter klasificiranje spletnih strani. Uporabili smo naslednje metode strojnega učenja: naivni Bayesov klasifikator, k-najbližjih sosedov, metodo naključnih gozdov, nevronsko mrežo in metodo podpornih vektorjev. Najbolj obetavne rezulate smo dobili z metodo podpornih vektorjev.

Language:Slovenian
Keywords:specializirani iskalnik, podatkovno rudarjenje, strojno učenje, spletne trgovine, analiza besedil, naivni Bayesov klasifikator, k-najbližjih sosedov, metoda naključnih gozdov, nevronska mreža, metoda podpornih vektorjev.
Work type:Bachelor thesis/paper (mb11)
Organization:FRI - Faculty of computer and information science
Year:2017
Publication date in RUL:24.01.2017
Views:898
Downloads:594
Metadata:XML RDF-CHPDL DC-XML DC-RDF
:
Share:AddThis
AddThis uses cookies that require your consent. Edit consent...

Secondary language

Language:English
Title:Search and classification of web shops
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.

Keywords:specialized search engine, data mining, machine learning, e- commerce, text analysis, naive Bayesian classifier, k-nearest neighbors algo- rithm, random forests, neural networks, support vector machine.

Similar documents

Similar works from RUL:
Similar works from other Slovenian collections:

Back