國立高雄大學圖資館 |

語系: 繁體中文

說明(常見問題)

圖資館首頁

登入

回首頁

切換: 標籤 | MARC模式 | ISBD

Probabilistic hashing techniques for...

Cornell University.

Probabilistic hashing techniques for big data.

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Probabilistic hashing techniques for big data.
作者:	Shrivastava, Anshumali.
面頁冊數:	193 p.
附註:	Source: Dissertation Abstracts International, Volume: 77-03(E), Section: B.
附註:	Adviser: Ping Li.
Contained By:	Dissertation Abstracts International77-03B(E).
標題:	Computer science.
電子資源:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3730467
ISBN:	9781339167350

Probabilistic hashing techniques for big data.
Shrivastava, Anshumali.

Probabilistic hashing techniques for big data. - 193 p.

Source: Dissertation Abstracts International, Volume: 77-03(E), Section: B.

Thesis (Ph.D.)--Cornell University, 2015.

We investigate probabilistic hashing techniques for addressing computational and memory challenges in large scale machine learning and data mining systems. In this thesis, we show that the traditional idea of hashing goes far beyond near-neighbor search and there are some striking new possibilities. We show that hashing can improve state of the art large scale learning algorithms, and it goes beyond the conventional notions of pairwise similarities. Despite being a very well studied topic in literature, we found several opportunities for fundamentally improving some of the well know textbook hashing algorithms. In particular, we show that the traditional way of computing minwise hashes is unnecessarily expensive and without loosing anything we can achieve an order of magnitude speedup. We also found that for cosine similarity search there is a better scheme than SimHash.

ISBN: 9781339167350Subjects--Topical Terms:

199325
Computer science.

Probabilistic hashing techniques for big data.
LDR:02436nmm a2200313 4500 001 476136
005 20160418090200.5
008 160517s2015 ||||||||||||||||| ||eng d
020 $a 9781339167350
035 $a (MiAaPQ)AAI3730467
035 $a AAI3730467
040 $a MiAaPQ $c MiAaPQ
100 1 $a Shrivastava, Anshumali. $3 730437
245 1 0 $a Probabilistic hashing techniques for big data.
300 $a 193 p.
500 $a Source: Dissertation Abstracts International, Volume: 77-03(E), Section: B.
500 $a Adviser: Ping Li.
502 $a Thesis (Ph.D.)--Cornell University, 2015.
520 $a We investigate probabilistic hashing techniques for addressing computational and memory challenges in large scale machine learning and data mining systems. In this thesis, we show that the traditional idea of hashing goes far beyond near-neighbor search and there are some striking new possibilities. We show that hashing can improve state of the art large scale learning algorithms, and it goes beyond the conventional notions of pairwise similarities. Despite being a very well studied topic in literature, we found several opportunities for fundamentally improving some of the well know textbook hashing algorithms. In particular, we show that the traditional way of computing minwise hashes is unnecessarily expensive and without loosing anything we can achieve an order of magnitude speedup. We also found that for cosine similarity search there is a better scheme than SimHash.
520 $a In the end, we show that the existing locality sensitive hashing framework itself is very restrictive, and we cannot have efficient algorithms for some important measures like inner products which are ubiquitous in machine learning. We propose asymmetric locality sensitive hashing (ALSH), an extended framework, where we show provable and practical efficient algorithms for Maximum Inner Product Search (MIPS). Having such an efficient solutions to MIPS directly scales up many popular machine learning algorithms.
520 $a We believe that this thesis provides significant improvements to some of the heavily used subroutines in big-data systems, which we hope will be adopted.
590 $a School code: 0058.
650 4 $a Computer science. $3 199325
650 4 $a Statistics. $3 182057
650 4 $a Information science. $3 190425
690 $a 0984
690 $a 0463
690 $a 0723
710 2 $a Cornell University. $b Computer Science. $3 730438
773 0 $t Dissertation Abstracts International $g 77-03B(E).
790 $a 0058
791 $a Ph.D.
792 $a 2015
793 $a English
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3730467