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Latent Representation and Sampling in Network: Application in Text Mining and Biology

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서명/저자사항Latent Representation and Sampling in Network: Application in Text Mining and Biology.
개인저자Saha, Tanay Kumar.
단체저자명Purdue University. Computer Sciences.
발행사항[S.l.]: Purdue University., 2018.
발행사항Ann Arbor: ProQuest Dissertations & Theses, 2018.
형태사항291 p.
기본자료 저록Dissertation Abstracts International 79-12B(E).
Dissertation Abstract International
ISBN9780438154506
학위논문주기Thesis (Ph.D.)--Purdue University, 2018.
일반주기 Source: Dissertation Abstracts International, Volume: 79-12(E), Section: B.
Advisers: Mohammad Al Hasan
요약In classical machine learning, hand-designed features are used for learning a mapping from raw data. However, human involvement in feature design makes the process expensive. Representation learning aims to learn abstract features directly from
요약In this dissertation, we propose models for incorporating temporal information given as a collection of networks from subsequent time-stamps. The primary objective of our models is to learn a better abstract feature representation of nodes and e
요약Besides applying to the network data, we also employ our models to incorporate extra-sentential information in the text domain for learning better representation of sentences. We build a context network of sentences to capture extra-sentential
요약A problem with the abstract features that we learn is that they lack interpretability. In real-life applications on network data, for some tasks, it is crucial to learn interpretable features in the form of graphical structures. For this we need
요약Finally, we show that we can use these frequent subgraph statistics and structures as features in various real-life applications. We show one application in biology and another in security. In both cases, we show that the structures and their st
일반주제명Computer science.
Artificial intelligence.
Information science.
언어영어
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