| LDR | | 00000nmm u2200205 4500 |
| 001 | | 000000332917 |
| 005 | | 20241206154806 |
| 008 | | 181129s2018 ||| | | | eng d |
| 020 | |
▼a 9780438154506 |
| 035 | |
▼a (MiAaPQ)AAI10808596 |
| 035 | |
▼a (MiAaPQ)purdue:22710 |
| 040 | |
▼a MiAaPQ
▼c MiAaPQ
▼d 248032 |
| 049 | 1 |
▼f DP |
| 082 | 0 |
▼a 004 |
| 100 | 1 |
▼a Saha, Tanay Kumar. |
| 245 | 10 |
▼a Latent Representation and Sampling in Network: Application in Text Mining and Biology. |
| 260 | |
▼a [S.l.] :
▼b Purdue University.,
▼c 2018 |
| 260 | 1 |
▼a Ann Arbor :
▼b ProQuest Dissertations & Theses,
▼c 2018 |
| 300 | |
▼a 291 p. |
| 500 | |
▼a Source: Dissertation Abstracts International, Volume: 79-12(E), Section: B. |
| 500 | |
▼a Advisers: Mohammad Al Hasan |
| 502 | 1 |
▼a Thesis (Ph.D.)--Purdue University, 2018. |
| 520 | |
▼a In 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 |
| 520 | |
▼a In 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 |
| 520 | |
▼a Besides 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 |
| 520 | |
▼a A 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 |
| 520 | |
▼a Finally, 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 |
| 590 | |
▼a School code: 0183. |
| 650 | 4 |
▼a Computer science. |
| 650 | 4 |
▼a Artificial intelligence. |
| 650 | 4 |
▼a Information science. |
| 690 | |
▼a 0984 |
| 690 | |
▼a 0800 |
| 690 | |
▼a 0723 |
| 710 | 20 |
▼a Purdue University.
▼b Computer Sciences. |
| 773 | 0 |
▼t Dissertation Abstracts International
▼g 79-12B(E). |
| 773 | |
▼t Dissertation Abstract International |
| 790 | |
▼a 0183 |
| 791 | |
▼a Ph.D. |
| 792 | |
▼a 2018 |
| 793 | |
▼a English |
| 856 | 40 |
▼u http://www.riss.kr/pdu/ddodLink.do?id=T14997824
▼n KERIS |
| 980 | |
▼a 201812
▼f 2019 |
| 990 | |
▼a 관리자
▼b 관리자 |