000041281 001__ 41281
000041281 005__ 20240124114339.0
000041281 037__ $$aETD
000041281 041__ $$aeng
000041281 245__ $$aCopula models for multivariate density estimation, classification and robust speech recognition
000041281 260__ $$bOregon Health and Science University
000041281 269__ $$a2018-12-13
000041281 336__ $$aDissertation
000041281 520__ $$aWe evaluate the performance of both of the proposed methods on several density estimation tasks from the UCI Repository as well as our corpus of Monkey vocalizations, recorded at the Oregon National Primate Research Center. We find that our methods represent the data consistently better than Gaussian mixture models with equivalent number of parameters. We also evaluate our proposed methods for building generative classifiers for a number of classification tasks from the UCI Repository. We find that these generative models perform as well or better than discriminative classifiers such as a Support Vector Machine (SVM).
000041281 540__ $$fCC BY
000041281 542__ $$fIn copyright - single owner
000041281 650__ $$aProbability Theory$$024607
000041281 650__ $$aModels, Statistical$$028242
000041281 650__ $$aSpeech Recognition Software$$036177
000041281 650__ $$aVocalization, Animal$$027896
000041281 6531_ $$acopula models
000041281 691__ $$aSchool of Medicine$$041369
000041281 692__ $$aCenter for Spoken Language Understanding$$041388
000041281 7001_ $$aBayestehtashk, Alireza$$uOregon Health and Science University$$041354
000041281 8564_ $$9139d502d-2a9b-4efe-8813-9013d5e69e2d$$s8394717$$uhttps://digitalcollections.ohsu.edu/record/41281/files/Bayestehtashk.Alriza.2018.pdf
000041281 909CO $$ooai:digitalcollections.ohsu.edu:41281$$pstudent-work
000041281 980__ $$aTheses and Dissertations
000041281 981__ $$aPublished$$b2023-07-13