S
Svetha Venkatesh
Researcher at Deakin University
Publications - 864
Citations - 20118
Svetha Venkatesh is an academic researcher from Deakin University. The author has contributed to research in topics: Bayesian optimization & Computer science. The author has an hindex of 60, co-authored 828 publications receiving 16441 citations. Previous affiliations of Svetha Venkatesh include Australian National University & National University of Singapore.
Papers
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Journal ArticleDOI
Cancer as a Tissue Anomaly: Classifying Tumor Transcriptomes Based Only on Healthy Data.
TL;DR: This report proposes using an established surveillance method that detects anomalous samples based on their deviation from a learned normal steady-state structure, and can create an anomaly detector for tissue transcriptomes, a “tissue detector,” that is capable of identifying cancer without ever seeing a single cancer example.
Journal ArticleDOI
Bayesian strategy selection identifies optimal solutions to complex problems using an example from GP prescribing
Steven Allender,Joshua Hayward,Sunil Gupta,Andrew Sanigorski,Santu Rana,Hugh Seward,Struan Jacobs,Svetha Venkatesh +7 more
TL;DR: An efficient way to test and identify optimal strategies from multiple possible solutions within a case study aiming to increase General Practitioner discussions of physical activity (PA) with their patients is demonstrated.
Proceedings ArticleDOI
Multiple hypotheses situation assessment
TL;DR: Presenting multiple hypotheses could avoid fixating on a single interpretation where better alternatives exist, and how to eliminate hypotheses selectively is looked at.
Journal ArticleDOI
Video sequence matching via decision tree path following
TL;DR: This paper presents an algorithm for resolution of a sequence of incrementally changing iconic queries, against a known database of model graphs, based on a representation using graphs and subgraph isomorphism detection.
Book ChapterDOI
An Application of Machine Learning Techniques for the Classification of Glaucomatous Progression
TL;DR: The use of new features for the data analysis combined with machine learning techniques to classify the medical data and the results of using decision trees to separate stable and progressive cases are described.