Displaying 1 to 20 from 20 results

gorse - A High Performance Recommender System Package based on Collaborative Filtering for Go

  •    Go

More examples could be found in the example folder. All models are tested by 5-fold cross validation on a PC with Intel(R) Core(TM) i5-4590 CPU (3.30GHz) and 16.0GB RAM. All scores are the best scores achieved by gorse yet.

n2 - TOROS N2 - lightweight approximate Nearest Neighbor library which runs faster even with large datasets

  •    C++

For more detail, see the installation for instruction on how to build N2 from source. N2 is an approximate nearest neighborhoods algorithm library written in C++ (including Python/Go bindings). N2 provides a much faster search speed than other implementations when modeling large dataset. Also, N2 supports multi-core CPUs for index building.

rbush-knn - k-nearest neighbors search (KNN) for RBush

  •    Javascript

k-nearest neighbors search for RBush. Implements a simple depth-first kNN search algorithm using a priority queue.

knn4qa - k-nearest neighbor search for question answering (QA) and information retrieval (IR)

  •    Java

This is a learning-to-rank pipeline, which is a part of the project where we study applicability of k-nearest neighbor search methods in IR and QA applications. This project is supported primarily by the NSF grant #1618159 : "Matching and Ranking via Proximity Graphs: Applications to Question Answering and Beyond". For more details, please, check the Wiki page.

python-timbl - python-timbl, originally developed by Sander Canisius, is a Python extension module wrapping the full TiMBL C++ programming interface

  •    Python

python-timbl is a Python extension module wrapping the full TiMBL C++ programming interface. With this module, all functionality exposed through the C++ interface is also available to Python scripts. Being able to access the API from Python greatly facilitates prototyping TiMBL-based applications. This is the 2013 release by Maarten van Gompel, building on the 2006 release by Sander Canisius. For those used to the old library, there is one backwards-incompatible change, adapt your scripts to use import timblapi instead of import timbl, as the latter is now a higher-level interface.

static-kdtree - A static kdtree data structure

  •    Javascript

kd-trees are a compact data structure for answering orthogonal range and nearest neighbor queries on higher dimensional point data in linear time. While they are not as efficient at answering orthogonal range queries as range trees - especially in low dimensions - kdtrees consume exponentially less space, support k-nearest neighbor queries and are relatively cheap to construct. This makes them useful in small to medium dimensions for achieving a modest speed up over a linear scan. It is also worth mentioning that for approximate nearest neighbor queries or queries with a fixed size radius, grids and locality sensitive hashing are strictly better options. In these charts the transition between "Medium" and "Big" depends on how many points there are in the data structure. As the number of points grows larger, the dimension at which kdtrees become practical goes up.

timbl - TiMBL implements several memory-based learning algorithms.

  •    C++

TiMBL is an open source software package implementing several memory-based learning algorithms, among which IB1-IG, an implementation of k-nearest neighbor classification with feature weighting suitable for symbolic feature spaces, and IGTree, a decision-tree approximation of IB1-IG. All implemented algorithms have in common that they store some representation of the training set explicitly in memory. During testing, new cases are classified by extrapolation from the most similar stored cases. For over fifteen years TiMBL has been mostly used in natural language processing as a machine learning classifier component, but its use extends to virtually any supervised machine learning domain. Due to its particular decision-tree-based implementation, TiMBL is in many cases far more efficient in classification than a standard k-nearest neighbor algorithm would be.

spark-knn - k-Nearest Neighbors algorithm on Spark

  •    Scala

WIP... k-Nearest Neighbors algorithm (k-NN) implemented on Apache Spark. This uses a hybrid spill tree approach to achieve high accuracy and search efficiency. The simplicity of k-NN and lack of tuning parameters makes k-NN a useful baseline model for many machine learning problems.

Machine_Learning - Estudo e implementação dos principais algoritmos de Machine Learning em Jupyter Notebooks

  •    Jupyter

Esse repositório foi criado com a intenção de difundir o ensino de Machine Learning em português. Os algoritmos aqui implementados não são otimizados e foram implementados visando o fácil entendimento. Portanto, não devem ser utilizados para fins de pesquisa ou outros fins além dos especificados.

knn-matting - Source Code for KNN Matting, CVPR 2012 / TPAMI 2013

  •    Matlab

run "bash install.sh" to download all the required libraries and data. It would take several minutes to tens of minutes, depending on the network connection. We have been running our codes since Matlab R2011b. The latest version of code is tested on Matlab R2015a. Please let us know if you run into problem.

kdtree - Absolute balanced kdtree for fast kNN search.

  •    C

This is a (nearly absolute) balanced kdtree for fast kNN search with bad performance for dynamic addition and removal. In fact we adopt quick sort to rebuild the whole tree after changes of the nodes. We cache the added or the deleted nodes which will not be actually mapped into the tree until the rebuild method to be invoked. The good thing is we can always keep the tree balanced, and the bad thing is we have to wait some time for the finish of tree rebuild. Moreover duplicated samples are allowed to be added with the tree still kept balanced. The thought of the implementation is posted here.

jsmlt - :factory: JavaScript Machine Learning Toolkit

  •    Javascript

The JavaScript Machine Learning Toolkit, or JSMLT, is an open source JavaScript library for education in machine learning. It implements several well-known supervised learning algorithms in an understandable, modular and well-commented way. Furthermore, visualization examples are provided which allow you to explore the way different machine learning algorithms work. Ultimately, JSMLT is intended to provide students with a better learning experience when studying machine learning algorithms. If you want to explore a visualization of the machine learning algorithms in JSMLT, check out visualml.io. It provides an interactive environment for using JSMLT's algorithms.

budget - A simply budget app that predicts where the expenses are being made

  •    Javascript

Budget is a JavaScript toy app that implements a KNN to predict where the expense is being made. An online version is hosted here. You can add it to your phone as an app, if you like.

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