From a54c2fd8df23a63d17dbb1126f8fd6ef59becf6f Mon Sep 17 00:00:00 2001 From: Leonid Boytsov Date: Tue, 4 Jun 2019 19:35:28 -0400 Subject: [PATCH] Update README.md --- README.md | 10 +++------- 1 file changed, 3 insertions(+), 7 deletions(-) diff --git a/README.md b/README.md index 2fb160f..95f9eef 100644 --- a/README.md +++ b/README.md @@ -24,18 +24,14 @@ NMSLIB is possibly the first library with a principled support for non-metric sp NMSLIB is an **extendible library**, which means that is possible to add new search methods and distance functions. NMSLIB can be used directly in C++ and Python (via Python bindings). In addition, it is also possible to build a query server, which can be used from Java (or other languages supported by Apache Thrift). Java has a native client, i.e., it works on many platforms without requiring a C++ library to be installed. -**Authors**: Bilegsaikhan Naidan, Leonid Boytsov, Yury Malkov. **With contributions from** David Novak, Lawrence Cayton, Wei Dong, Avrelin Nikita, Ben Frederickson, Dmitry Yashunin, Bob Poekert, @orgoro, @gregfriedland, Maxim Andreev, Daniel Lemire, Nathan Kurz, Alexander Ponomarenko. +**Authors**: Bilegsaikhan Naidan, Leonid Boytsov, Yury Malkov. **With contributions from** David Novak, Lawrence Cayton, Wei Dong, Avrelin Nikita, Ben Frederickson, Dmitry Yashunin, Bob Poekert, @orgoro, @gregfriedland, +Scott Gigante, Maxim Andreev, Daniel Lemire, Nathan Kurz, Alexander Ponomarenko. ## Brief History NMSLIB started as a personal project of Bilegsaikhan Naidan, who created the initial code base, the Python bindings, and participated in earlier evaluations. -The most successful class of methods--neighborhood/proximity graphs--is represented by the Hierarchical Navigable Small World Graph (HNSW) -due to Malkov and Yashunin (see the publications below). -Other most useful methods, include a modification of the VP-tree -due to Boytsov and Naidan (2013), -a Neighborhood APProximation index (NAPP) proposed by Tellez et al. (2013) and improved by David Novak, -as well as a vanilla uncompressed inverted file. +The most successful class of methods--neighborhood/proximity graphs--is represented by the Hierarchical Navigable Small World Graph (HNSW) due to Malkov and Yashunin (see the publications below). Other most useful methods, include a modification of the VP-tree due to Boytsov and Naidan (2013), a Neighborhood APProximation index (NAPP) proposed by Tellez et al. (2013) and improved by David Novak, as well as a vanilla uncompressed inverted file. ## Credits and Citing