Showing 1035 open source projects for "android open source"

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  • 1
    Adaptive Gaussian Filtering

    Adaptive Gaussian Filtering

    Machine learning with Gaussian kernels.

    Libagf is a machine learning library that includes adaptive kernel density estimators using Gaussian kernels and k-nearest neighbours. Operations include statistical classification, interpolation/non-linear regression and pdf estimation. For statistical classification there is a borders training feature for creating fast and general pre-trained models that nonetheless return the conditional probabilities. Libagf also includes clustering algorithms as well as comparison and validation...
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  • 2

    SuRankCo

    Supervised Ranking of Contigs in de novo Assemblies

    SuRankCo is a machine learning based software to score and rank contigs from de novo assemblies of next generation sequencing data. It trains with alignments of contigs with known reference genomes and predicts scores and ranking for contigs which have no related reference genome yet. For more details about SuRankCo and its functioning, please see "SuRankCo: Supervised Ranking of Contigs in de novo Assemblies" Mathias Kuhring, Piotr Wojtek Dabrowski, Andreas Nitsche and Bernhard Y....
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  • 3
    nn22 Basic Neural Networks for Octave

    nn22 Basic Neural Networks for Octave

    Simple .m files, Basic Neural Networks study for Octave (or Matlab)

    --> For a more detailed description check the README text under the 'Files' menu option :) The project consists of a few very simple .m files for a Basic Neural Networks study under Octave (or Matlab). The idea is to provide a context for beginners that will allow to develop neural networks, while at the same time get to see and feel the behavior of a basic neural networks' functioning. The code is completely open to be modified and may suit several scenarios. The code...
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  • 4

    LexSub

    A Lexical Substitution Framework

    Lexical substitution framework for supervised all-words lexical substitution using delexicalized features. For a runnable (but GPL-licensed) version of LexSub, see LexSub-GPL (sf.net/p/lexsub/lexsub-gpl)
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  • 5

    Automatic cell lineage reconstruction

    Automatic segmentation and tracking for 3D time-lapse microscopy

    From Amat et al., Nature Methods, 2014*: "The comprehensive reconstruction of cell lineages in complex multicellular organisms is a central goal of developmental biology. We present an open-source computational framework for segmentation and tracking of cell nuclei with high accuracy and speed. We demonstrate its (1) generality, by reconstructing cell lineages in four-dimensional, terabyte-sized image data of fruit-fly, zebrafish and mouse embryos, acquired with three different types of fluorescence microscopes, (2) scalability, by analyzing advanced stages of development with up to 20,000 cells per time point, at 26,000 cells min-1 on a single computer workstation, and (3) ease of use, by adjusting only two parameters across all data sets and providing visualization and editing tools for efficient data curation. ...
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  • 6
    IMAGINE

    IMAGINE

    Biological image viewer and processor

    Detection, enumeration, and sizing of biological organisms by image analysis.
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  • 7

    Fingerprint Recognition System

    Fingerprint Recognition System 5.3 - Matlab source code

    The proposed filter-based algorithm uses a bank of Gabor filters to capture both local and global details in a fingerprint as a compact fixed length FingerCode. The fingerprint matching is based on the Euclidean distance between the two corresponding FingerCodes and hence is extremely fast. We are able to achieve a verification accuracy which is only marginally inferior to the best results of minutiae-based algorithms published in the open literature. Our system performs better than a...
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  • 8
    Intelligent Keyword Miner

    Intelligent Keyword Miner

    Intelligent SEO keyword miner and predicing tool

    THIS IS A NETBEANS 8.02 PROJECT ENGLISH ONLY This program was made to help me with the patent research. It simply generates the search keywords, based on your upvotes or a downvotes of the input parameters. It can accept a text or URL (text takes a prescedence over the URL). If you input URL, it goes to a page, and learns its text from HTML format. This program is intelligent as it predicts what you may want to search next, based on your personal trends. After searching the...
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  • 9
    MODLEM

    MODLEM

    rule-based, WEKA compatible, Machine Learning algorithm

    This project is a WEKA (Waikato Environment for Knowledge Analysis) compatible implementation of MODLEM - a Machine Learning algorithm which induces minimum set of rules. These rules can be adopted as a classifier (in terms of ML). It is a sequential covering algorithm, which was invented to cope with numeric data without discretization. Actually the nominal and numeric attributes are treated in the same way: attribute's space is being searched to find the best rule condition during rule...
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  • 10

    Chordalysis

    Log-linear analysis (data modelling) for high-dimensional data

    ...Chordalysis makes it possible to discover the structure of datasets with thousands of variables on a standard desktop computer. Associated papers at ICDM 2013, ICDM 2014 and SDM 2015 can be found at http://www.francois-petitjean.com/Research/ YourKit is supporting Chordalysis open source project with its full-featured Java Profiler. YourKit is the creator of innovative and intelligent tools for profiling Java and .NET applications. http://www.yourkit.com
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  • 11
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  • 12

    LWPR

    Locally Weighted Projection Regression (LWPR)

    Locally Weighted Projection Regression (LWPR) is a fully incremental, online algorithm for non-linear function approximation in high dimensional spaces, capable of handling redundant and irrelevant input dimensions. At its core, it uses locally linear models, spanned by a small number of univariate regressions in selected directions in input space. A locally weighted variant of Partial Least Squares (PLS) is employed for doing the dimensionality reduction. Please cite: [1] Sethu...
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  • 13
    Qix

    Qix

    Machine Learning, Deep Learning, PostgreSQL, Distributed System

    Qix is an open-source repository that aggregates resources related to artificial intelligence, machine learning, and deep learning development. The project is designed as a curated index of useful tools, frameworks, research papers, and learning materials that developers can use to explore the AI ecosystem. It organizes information about libraries, tutorials, datasets, and software tools used in modern AI research and development.
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  • 14

    KMeansAniX

    Animation of kmeans clustering using X Window System

    Open source animation of kmeans clustering in X Window System using the C++ libplotter library. Supports Linux, Mac, and BSD. Includes common initialization methods such as Forgy, Macqueen, random, and angular. Sample videos are available through the Files Tab above. The SVN repo is accessible thorugh the Code Tab above. Requires a C++ compiler, libplot-dev, and libncurses5-dev Mac alternative to libplot-dev: macports plotutils +x11
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  • 15
    Marsyas (Music Analysis, Retrieval and Synthesis for Audio Signals) is a framework for developing systems for audio processing. It provides an general architecture for connecting audio, soundfiles, signal processing blocks and machine learning. Source code at SF is outdated! Marsyas is now hosted at GitHub: https://github.com/marsyas/marsyas Downloads are now provided at Bintray: https://bintray.com/marsyas
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  • 16
    ConvNetJS

    ConvNetJS

    Deep learning in Javascript to train convolutional neural networks

    ConvNetJS is a Javascript library for training Deep Learning models (Neural Networks) entirely in your browser. Open a tab and you're training. No software requirements, no compilers, no installations, no GPUs, no sweat. ConvNetJS is an implementation of Neural networks, together with nice browser-based demos. It currently supports common Neural Network modules (fully connected layers, non-linearities), classification (SVM/Softmax) and Regression (L2) cost functions, ability to specify and...
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  • 17
    BudgetedSVM

    BudgetedSVM

    BudgetedSVM: A C++ Toolbox for Large-scale, Non-linear Classification

    We present BudgetedSVM, a C++ toolbox containing highly optimized implementations of three recently proposed algorithms for scalable training of Support Vector Machine (SVM) approximators: Adaptive Multi-hyperplane Machines (AMM), Budgeted Stochastic Gradient Descent (BSGD), and Low-rank Linearization SVM (LLSVM). BudgetedSVM trains models with accuracy comparable to LibSVM in time comparable to LibLinear, as it allows solving highly non-linear classi fication problems with millions of...
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  • 18

    ANNFiD

    A forensic file identification tool using neural networks

    Just carved a bunch of bytes and have no idea what they could be? Maybe ANNFiD can help. ANNFiD uses neural network to identify byte patterns. It can be trained and has a GUI to help in the process. The tool is still on a very early stage, but could improve exponentially with the help of the developer community
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  • 19

    EducationalLCS

    eLCS - Educational Learning Classifier System

    Educational Learning Classifier System (eLCS) is a set of learning classifier system (LCS) educational demos designed to introduce students or researchers to the basics of a modern Michigan-style LCS algorithm. This eLCS package includes 5 different implementations of a basic LCS algorithm, as part of a 6 stage set of demos that will be paired with the first introductory LCS textbook. Each eLCS implementations (from demo 2 up to demo 6) progressively add major components of the entire...
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  • 20
    C-IL2P

    C-IL2P

    Original C-IL2P

    This is a C++ implementation of the original C-IL2P system, invented by Artur D'Avila Garcez and Gerson Zaverucha. C-IL2P is a neural-symbolic learning system which uses a propositional logic program to create a three-layer recursive neural network and uses back-propagation to learn from examples.
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  • 21

    StabLe

    An algorithm for learning stable graphical models from data

    Stable Graphical Model Learning (StabLe) is an algorithm for learning the structure and parameters of stable graphical (SG) models from data. Stable random variables are motivated by the central limit theorem for densities with (potentially) unbounded variance and can be thought of as natural generalizations of the Gaussian distribution to skewed and heavy-tailed phenomenon. SG models are multi-variate stable distributions that represent Bayesian networks whose edges encode linear...
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  • 22

    ProximityForest

    Efficient Approximate Nearest Neighbors for General Metric Spaces

    A proximity forest is a data structure that allows for efficient computation of approximate nearest neighbors of arbitrary data elements in a metric space. See: O'Hara and Draper, "Are You Using the Right Approximate Nearest Neighbor Algorithm?", WACV 2013 (best student paper award). One application of a ProximityForest is given in the following CVPR publication: Stephen O'Hara and Bruce A. Draper, "Scalable Action Recognition with a Subspace Forest," IEEE Conference on Computer...
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  • 23

    Bermuda Text-to-Speech

    This project includes basic NLP and DSP techniques for Text-to-Speech

    See TTS demo at: http://rslp.racai.ro/index.php?page=tts This is an entirely written in JAVA project which includes a set of tools and methods designed to enable Multilingual Text-to-Speech (TTS) synthesis. We currently support English and Romanian but we will soon train more models and make them available for download. If you want to read more about our other NLP and TTS tools check out http://nlptools.racai.ro.
    Downloads: 1 This Week
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  • 24

    Graphlet kernel framework

    Calculates similarity between neighborhoods of two vertices in a graph

    This software package provides a framework for calculating similarity between neighborhoods rooted at two vertices of interest in a labeled graph (undirected or directed). The list of available similarity functions includes: cumulative random walk, standard random walk, standard graphlet kernel, edit distance graphlet kernel, label substitution graphlet kernel and edge indel graphlet kernel. The graphlet kernel framework can be used for vertex (node) classification in graphs, kernel-based...
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  • 25
    The Edlin toolkit provides a machine learning framework for linear models, designed to be easy to read and understand. The main goal is to provide an architecture and easy to edit working examples of implementations for popular learning algorithms.
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