Person:
YENİTERZİ, Reyyan

Profile Picture

Email Address

Birth Date

WoSScopusGoogle ScholarORCID

Name

Job Title

First Name

Reyyan

Last Name

YENİTERZİ
OrgUnit Logo
Organizational Unit

Filters

2018 - 20192
Reset filters

Settings

Publication Search Results

Now showing 1 - 2 of 2
  • Placeholder
    Conference ObjectPublicationMetadata only
    MedSpecSearch: Medical specialty search
    (Springer Nature, 2019) Şahin, Mehmet Uluç; Balatkan, Eren; Eran, Cihan; Zeydan, E.; Yeniterzi, Reyyan; Computer Science; YENİTERZİ, Reyyan; Şahin, Mehmet Uluç; Balatkan, Eren; Eran, Cihan
    MedSpecSearch (www.medspecsearch.com) is a search engine for helping users to find the relevant medical specialty for a doctor visit based on users’ description of symptoms. This system is useful for users who are not sure of which medical specialty they should consult to. Furthermore, the API of the search engine can be used as part of the online doctor appointment and medical consultation sites to route the patient or question to the right medical specialty. The system returns the top three relevant specialties when the estimated confidence score is high. Otherwise, it asks users to input more data.
  • Placeholder
    ArticlePublicationMetadata only
    Supervising topic models with Gaussian processes
    (Elsevier, 2018-05) Kandemir, Melih; Kekeç, T.; Yeniterzi, Reyyan; Computer Science; KANDEMİR, Malih; YENİTERZİ, Reyyan
    Topic modeling is a powerful approach for modeling data represented as high-dimensional histograms. While the high dimensionality of such input data is extremely beneficial in unsupervised applications including language modeling and text data exploration, it introduces difficulties in cases where class information is available to boost up prediction performance. Feeding such input directly to a classifier suffers from the curse of dimensionality. Performing dimensionality reduction and classification disjointly, on the other hand, cannot enjoy optimal performance due to information loss in the gap between these two steps unaware of each other. Existing supervised topic models introduced as a remedy to such scenarios have thus far incorporated only linear classifiers in order to keep inference tractable, causing a dramatical sacrifice from expressive power. In this paper, we propose the first Bayesian construction to perform topic modeling and non-linear classification jointly. We use the well-known Latent Dirichlet Allocation (LDA) for topic modeling and sparse Gaussian processes for non-linear classification. We combine these two components by a latent variable encoding the empirical topic distribution of each document in the corpus. We achieve a novel variational inference scheme by adapting ideas from the newly emerging deep Gaussian processes into the realm of topic modeling. We demonstrate that our model outperforms other existing approaches such as: (i) disjoint LDA and non-linear classification, (ii) joint LDA and linear classification, (iii) joint non-LDA linear subspace modeling and linear classification, and (iv) non-linear classification without topic modeling, in three benchmark data sets from two real-world applications: text categorization and image tagging.