| 000 | 05427nam a2200697 i 4500 | ||
|---|---|---|---|
| 001 | 7302713 | ||
| 003 | IEEE | ||
| 005 | 20200413152918.0 | ||
| 006 | m eo d | ||
| 007 | cr cn |||m|||a | ||
| 008 | 150917s2015 cau foab 000 0 eng d | ||
| 020 |
_a9781627058094 _qebook |
||
| 020 |
_z9781627058087 _qprint |
||
| 024 | 7 |
_a10.2200/S00661ED1V01Y201508ICR044 _2doi |
|
| 035 | _a(CaBNVSL)swl00405557 | ||
| 035 | _a(OCoLC)921518060 | ||
| 040 |
_aCaBNVSL _beng _erda _cCaBNVSL _dCaBNVSL |
||
| 050 | 4 |
_aTK5105.884 _b.M256 2015 |
|
| 082 | 0 | 4 |
_a025.04 _223 |
| 100 | 1 |
_aManasse, Mark S., _eauthor. |
|
| 245 | 1 | 0 |
_aOn the efficient determination of most near neighbors : _bhorseshoes, hand grenades, Web search, and other situations when close is close enough / _cMark S. Manasse. |
| 250 | _aSecond edition. | ||
| 264 | 1 |
_aSan Rafael, California (1537 Fourth Street, San Rafael, CA 94901 USA) : _bMorgan & Claypool, _c2015. |
|
| 300 | _a1 PDF (xix, 80 pages) | ||
| 336 |
_atext _2rdacontent |
||
| 337 |
_aelectronic _2isbdmedia |
||
| 338 |
_aonline resource _2rdacarrier |
||
| 490 | 1 |
_aSynthesis lectures on information concepts, retrieval, and services, _x1947-9468 ; _v# 44 |
|
| 538 | _aMode of access: World Wide Web. | ||
| 538 | _aSystem requirements: Adobe Acrobat Reader. | ||
| 500 | _aPart of: Synthesis digital library of engineering and computer science. | ||
| 504 | _aIncludes bibliographical references (pages 75-77). | ||
| 505 | 0 | _a1. Introduction -- 1.1 On similarity, resemblance, look-alikes, and entity resolution -- 1.2 You must know at least this much math to read this book -- 1.3 Cumulative distribution and probability density functions -- | |
| 505 | 8 | _a2. Comparing web pages for similarity: an overview -- 2.1 Choosing the features of a web page to compare -- 2.2 Turning features into integers (Rabin hashing) -- 2.3 How should we measure the proximity of features? -- 2.4 Feature reduction -- 2.5 Putting it together with supershingling -- | |
| 505 | 8 | _a3. A personal history of web search -- 3.1 Complexity issues and implementation -- 3.2 Implementing duplicate suppression -- 3.3 Rabin hashing revisited -- | |
| 505 | 8 | _a4. Uniform sampling after Alta Vista -- 4.1 Using less randomness to improve sampling efficiency -- 4.2 Conjectures vs. theorems -- 4.3 Finding the first point of divergence efficiently -- 4.4 Uniform consistent sampling summarized -- | |
| 505 | 8 | _a5. Why weight (and how)? -- 5.1 Constant expected-time consistent weighted sampling -- 5.2 Constant time consistent weighted sampling -- 5.3 Accelerating weighted sampling -- | |
| 505 | 8 | _a6. A few applications -- 6.1 Web deduplication -- 6.2 File systems: winnowing and friends -- 6.3 Further applications -- | |
| 505 | 8 | _a7. Forks in the road: Flajolet and slightly biased sampling -- 7.1 Flajolet-Martin -- 7.2 Li's rediscovery -- 7.3 Approximation by randomized rounding -- 7.4 Scaling -- | |
| 505 | 8 | _aAfterword -- Bibliography -- Author's biography. | |
| 506 | 1 | _aAbstract freely available; full-text restricted to subscribers or individual document purchasers. | |
| 510 | 0 | _aCompendex | |
| 510 | 0 | _aINSPEC | |
| 510 | 0 | _aGoogle scholar | |
| 510 | 0 | _aGoogle book search | |
| 520 | 3 | _aThe time-worn aphorism "close only counts in horseshoes and hand grenades" is clearly inadequate. Close also counts in golf, shuffleboard, archery, darts, curling, and other games of accuracy in which hitting the precise center of the target isn't to be expected every time, or in which we can expect to be driven from the target by skilled opponents. This book is not devoted to sports discussions, but to efficient algorithms for determining pairs of closely related web pages, and a few other situations in which we have found that inexact matching is good enough - where proximity suffices. We will not, however, attempt to be comprehensive in the investigation of probabilistic algorithms, approximation algorithms, or even techniques for organizing the discovery of nearest neighbors. We are more concerned with finding nearby neighbors; if they are not particularly close by, we are not particularly interested. In thinking of when approximation is sufficient, remember the oft-told joke about two campers sitting around after dinner. They hear noises coming towards them. One of them reaches for a pair of running shoes, and starts to don them. The second then notes that even with running shoes, they cannot hope to outrun a bear, to which the first notes that most likely the bear will be satiated after catching the slower of them. We seek problems in which we don't need to be faster than the bear, just faster than the others fleeing the bear. | |
| 530 | _aAlso available in print. | ||
| 588 | _aTitle from PDF title page (viewed on September 17, 2015). | ||
| 650 | 0 | _aInternet searching. | |
| 650 | 0 | _aNearest neighbor analysis (Statistics) | |
| 650 | 0 | _aStatistical matching. | |
| 653 | _anearest neighbor | ||
| 653 | _asearch algorithms | ||
| 653 | _ainformation retrieval | ||
| 653 | _aIR | ||
| 653 | _amulti-dimensional | ||
| 776 | 0 | 8 |
_iPrint version: _z9781627058087 |
| 830 | 0 | _aSynthesis digital library of engineering and computer science. | |
| 830 | 0 |
_aSynthesis lectures on information concepts, retrieval, and services ; _v# 44. _x1947-9468 |
|
| 856 | 4 | 2 |
_3Abstract with links to resource _uhttp://ieeexplore.ieee.org/servlet/opac?bknumber=7302713 |
| 999 |
_c562157 _d562157 |
||