| LDR | | 02132nam u200421 4500 |
| 001 | | 000000421444 |
| 005 | | 20190215165323 |
| 008 | | 181129s2018 |||||||||||||||||c||eng d |
| 020 | |
▼a 9780438026636 |
| 035 | |
▼a (MiAaPQ)AAI10814646 |
| 035 | |
▼a (MiAaPQ)cornellgrad:10809 |
| 040 | |
▼a MiAaPQ
▼c MiAaPQ
▼d 247004 |
| 082 | 0 |
▼a 004 |
| 100 | 1 |
▼a Yuan, Yang. |
| 245 | 10 |
▼a Provable and Practical Algorithms for Non-Convex Problems in Machine Learning. |
| 260 | |
▼a [S.l.]:
▼b Cornell University.,
▼c 2018. |
| 260 | 1 |
▼a Ann Arbor:
▼b ProQuest Dissertations & Theses,
▼c 2018. |
| 300 | |
▼a 204 p. |
| 500 | |
▼a Source: Dissertation Abstracts International, Volume: 79-10(E), Section: B. |
| 500 | |
▼a Adviser: Robert David Kleinberg. |
| 502 | 1 |
▼a Thesis (Ph.D.)--Cornell University, 2018. |
| 520 | |
▼a Machine learning has become one of the most exciting research areas in the world, with various applications. However, there exists a noticeable gap between theory and practice. On one hand, a simple algorithm like stochastic gradient descent (SG |
| 520 | |
▼a This dissertation is about bridging the gap between theory and practice from two directions. The first direction is "practice to theory", i.e., to explain and analyze the existing algorithms and empirical observations in machine learning. Along |
| 520 | |
▼a The other direction is "theory to practice", i.e., using theoretical tools to obtain new, better and practical algorithms. Along this direction, we introduce a new algorithm Harmonica that uses Fourier analysis and compressed sensing for tuning |
| 590 | |
▼a School code: 0058. |
| 650 | 4 |
▼a Computer science. |
| 650 | 4 |
▼a Artificial intelligence. |
| 690 | |
▼a 0984 |
| 690 | |
▼a 0800 |
| 710 | 20 |
▼a Cornell University.
▼b Computer Science. |
| 773 | 0 |
▼t Dissertation Abstracts International
▼g 79-10B(E). |
| 773 | |
▼t Dissertation Abstract International |
| 790 | |
▼a 0058 |
| 791 | |
▼a Ph.D. |
| 792 | |
▼a 2018 |
| 793 | |
▼a English |
| 856 | 40 |
▼u http://www.riss.kr/pdu/ddodLink.do?id=T14998133
▼n KERIS
▼z 이 자료의 원문은 한국교육학술정보원에서 제공합니다. |
| 980 | |
▼a 201812
▼f 2019 |
| 990 | |
▼a ***1012033 |