Title
Treniranje strukturnih klasifikatora za različite funkcije gubitaka sa primenom na probleme klasifikovanja sekvenci : doktorska disertacija
Creator
Mančev, Dejan, 1985-
Copyright date
2015
Object Links
Select license
Autorstvo-Nekomercijalno-Bez prerade 3.0 Srbija (CC BY-NC-ND 3.0)
License description
Dozvoljavate samo preuzimanje i distribuciju dela, ako/dok se pravilno naznačava ime autora, bez ikakvih promena dela i bez prava komercijalnog korišćenja dela. Ova licenca je najstroža CC licenca. Osnovni opis Licence: http://creativecommons.org/licenses/by-nc-nd/3.0/rs/deed.sr_LATN. Sadržaj ugovora u celini: http://creativecommons.org/licenses/by-nc-nd/3.0/rs/legalcode.sr-Latn
Language
English
Cobiss-ID
Theses Type
PhD thesis
description
Datum odbrane: 06.07.2015.
Other responsibilities
mentor
Todorović, Branimir 1967-
član komisije
Ćirić, Miroslav 1964-
član komisije
Stanimirović, Predrag 1959-
član komisije
Stanković, Miomir
član komisije
Stoimenov, Leonid
Academic Expertise
Prirodno-matematičke nauke
Academic Title
-
University
Univerzitet u Nišu
Faculty
Prirodno-matematički fakultet
Group
Odsek za matematiku i informatiku
Title translated
Training Structured Classifiers for Different Loss Functions with the Application to Sequence Labeling Problems
Publisher
Niš : [D. Mančev]
Format
PDF/A (144 lista)
description
Umnoženo za odbranu.
Univerzitet u Nišu, Prirodno-matematički fakultet, 2015.
Bibliografija: str. 87-93.
Izvod ; Abstract.
description
artificial intelligence
Abstract (en)
This thesis presents algorithms for training structured classifiers over different loss functions. It introduces a new primal subgradient method for the optimization of average sum loss, several extensions of max-margin classifiers to the k-best case, a sequential dual method for the structured ramp loss optimization, It considers different decoding algorithms over semirings and presents an organization of a two-structured-model committee in order to improve the results. It includes theoretical analysis of introduced algorithms, as well as experimental results on sequence labelling problems in natural language processing.
Authors Key words
Računarske nauke, strukturno učenje, klasifikacija sekvenci
Authors Key words
structured learning, sequence labeling
Classification
004.8
Type
Elektronska teza
Abstract (en)
This thesis presents algorithms for training structured classifiers over different loss functions. It introduces a new primal subgradient method for the optimization of average sum loss, several extensions of max-margin classifiers to the k-best case, a sequential dual method for the structured ramp loss optimization, It considers different decoding algorithms over semirings and presents an organization of a two-structured-model committee in order to improve the results. It includes theoretical analysis of introduced algorithms, as well as experimental results on sequence labelling problems in natural language processing.
“Data exchange” service offers individual users metadata transfer in several different formats. Citation formats are offered for transfers in texts as for the transfer into internet pages. Citation formats include permanent links that guarantee access to cited sources. For use are commonly structured metadata schemes : Dublin Core xml and ETUB-MS xml, local adaptation of international ETD-MS scheme intended for use in academic documents.