Displaying 1 to 9 from 9 results

machine_learning_basics - Plain python implementations of basic machine learning algorithms

  •    Jupyter

This repository contains implementations of basic machine learning algorithms in plain Python (Python Version 3.6+). All algorithms are implemented from scratch without using additional machine learning libraries. The intention of these notebooks is to provide a basic understanding of the algorithms and their underlying structure, not to provide the most efficient implementations. After several requests I started preparing notebooks on how to preprocess datasets for machine learning. Within the next months I will add one notebook for each kind of dataset (text, images, ...). As before, the intention of these notebooks is to provide a basic understanding of the preprocessing steps, not to provide the most efficient implementations.

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.

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.

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.

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.

art - Exploring the connections between artworks with deep "Visual Analogies"

  •    TypeScript

Art is one of the few languages which transcends barriers of country, culture, and time. We aim to create an algorithm that can help discover the common semantic elements of art even between any culture, media, artist, or collection within the combined artworks of The Metropolitan Museum of Art and The Rijksmusem. Image retrieval systems allow individuals to find images that are semantically similar to a query image. This serves as the backbone of reverse image search engines and many product recommendation engines. We present a novel method for specializing image retrieval systems called conditional image retrieval. When applied over large art datasets, conditional image retrieval provides visual analogies that bring to light hidden connections among different artists, cultures, and media. Conditional image retrieval systems can efficiently find shared semantics between works of vastly different media and cultural origin. Our paper introduces new variants of K-Nearest Neighbor algorithms that support specializing to particular subsets of image collections on the fly.

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