Learning during study and test A joint evaluation of list length effects and output interference

Date

2015-11-18

Author

Crıss, Amy
Kılıç Özhan, Aslı
Malmberg, Kenneth
Fontaıne, Jessıca

Metadata

Show full item record

Item Usage Stats

35
views

0
downloads

URI

https://hdl.handle.net/11511/81109

Collections

Unverified, Conference / Seminar

Suggestions

OpenMETU
Core

Learning-based robust sample selection to reduce noise in high dimensional transcriptome data Kızılilsoley, Nehir; Tanıl, Ezgi; Nikerel, Emrah (Orta Doğu Teknik Üniversitesi Enformatik Enstitüsü; 2022-10) To reduce inherent noise in high dimensional transcriptome data from a lung cancer cohort, a learning based sub-sample selection approach is adopted. Focusing on consensus clustering analysis, TCGA network data on lung cancer reached its maximum cluster stability when divided into three, which matches with the number of actual groups (adenocarcinoma, squamous cell carcinoma and normal). Using silhouette width as well as naive inspection of clustering performance to filter out samples, 840 out of 1145 sample...
Learning Smooth Pattern Transformation Manifolds Vural, Elif (2013-04-01) Manifold models provide low-dimensional representations that are useful for processing and analyzing data in a transformation-invariant way. In this paper, we study the problem of learning smooth pattern transformation manifolds from image sets that represent observations of geometrically transformed signals. To construct a manifold, we build a representative pattern whose transformations accurately fit various input images. We examine two objectives of the manifold-building problem, namely, approximation a...
Learning semi-supervised nonlinear embeddings for domain-adaptive pattern recognition Vural, Elif (null; 2019-05-20) We study the problem of learning nonlinear data embeddings in order to obtain representations for efficient and domain-invariant recognition of visual patterns. Given observations of a training set of patterns from different classes in two different domains, we propose a method to learn a nonlinear mapping of the data samples from different domains into a common domain. The nonlinear mapping is learnt such that the class means of different domains are mapped to nearby points in the common domain in order to...
Learning Data Delivery Paths in QoI-Aware Information-Centric Sensor Networks Singh, Gayathri Tilak; Al-Turjman, Fadi M. (2016-08-01) In this paper, we envision future sensor networks to be operating as information-gathering networks in large-scale Internet-of-Things applications such as smart cities, which serve multiple users with diverse quality-of-information (QoI) requirements on the data delivered by the network. To learn data delivery paths that dynamically adapt to changing user requirements in this information-centric sensor network (ICSN) environment, we make use of cognitive nodes that implement both learning and reasoning in t...
Learning customized and optimized lists of rules with mathematical programming Rudin, Cynthia; Ertekin Bolelli, Şeyda (Springer Science and Business Media LLC, 2018-12-01) We introduce a mathematical programming approach to building rule lists, which are a type of interpretable, nonlinear, and logical machine learning classifier involving IF-THEN rules. Unlike traditional decision tree algorithms like CART and C5.0, this method does not use greedy splitting and pruning. Instead, it aims to fully optimize a combination of accuracy and sparsity, obeying user-defined constraints. This method is useful for producing non-black-box predictive models, and has the benefit of a clear ...

Citation Formats

A. Crıss, A. Kılıç Özhan, K. Malmberg, and J. Fontaıne, “Learning during study and test A joint evaluation of list length effects and output interference,” 2015, Accessed: 00, 2021. [Online]. Available: https://hdl.handle.net/11511/81109.