Open Source MATLAB Machine Learning Software - Page 2

MATLAB Machine Learning Software

View 446 business solutions

Browse free open source MATLAB Machine Learning Software and projects below. Use the toggles on the left to filter open source MATLAB Machine Learning Software by OS, license, language, programming language, and project status.

  • Cloud-based help desk software with ServoDesk Icon
    Cloud-based help desk software with ServoDesk

    Full access to Enterprise features. No credit card required.

    What if You Could Automate 90% of Your Repetitive Tasks in Under 30 Days? At ServoDesk, we help businesses like yours automate operations with AI, allowing you to cut service times in half and increase productivity by 25% - without hiring more staff.
    Try ServoDesk for free
  • Keep company data safe with Chrome Enterprise Icon
    Keep company data safe with Chrome Enterprise

    Protect your business with AI policies and data loss prevention in the browser

    Make AI work your way with Chrome Enterprise. Block unapproved sites and set custom data controls that align with your company's policies.
    Download Chrome
  • 1
    DOGMA is a MATLAB toolbox for discriminative online learning. It implements all the state of the art algorithms in a unique and simple framework. Examples are Perceptron, Passive-Aggresive, ALMA, NORMA, SILK, Projectron, RBP, Banditron, etc.
    Downloads: 0 This Week
    Last Update:
    See Project
  • 2
    Deep Photo Style Transfer

    Deep Photo Style Transfer

    Code and data for paper "Deep Photo Style Transfer"

    Deep Photo Style Transfer is an implementation of the algorithm described in the paper “Deep Photo Style Transfer” (arXiv 1703.07511). The software allows users to transfer the style of one photograph to another while preserving photorealism and semantic consistency. It relies on semantic segmentation masks to guide style transfer (so that e.g. sky maps to sky, building maps to building), and uses a matting Laplacian regularization term to ensure smooth transitions. The repository provides code in Torch (Lua), MATLAB / Octave scripts for computing the Laplacian, and pre-trained models. Pretrained models and example scripts for ease of use. Compatibility with MATLAB / Octave for Laplacian computations.
    Downloads: 0 This Week
    Last Update:
    See Project
  • 3
    DeepLearnToolbox

    DeepLearnToolbox

    Matlab/Octave toolbox for deep learning

    DeepLearnToolbox is a MATLAB / Octave toolbox for prototyping deep learning models. It provides implementations of feedforward neural networks, convolutional neural networks (CNNs), deep belief networks (DBNs), stacked autoencoders, convolutional autoencoders, and more. The toolbox includes example scripts for each method, enabling users to quickly experiment with architectures, training, and inference workflows. Although it's been flagged as deprecated and no longer actively maintained, it is still used for educational and prototyping purposes. Deep belief networks (DBN) and restricted Boltzmann machines (RBM). Example scripts demonstrating usage.
    Downloads: 0 This Week
    Last Update:
    See Project
  • 4
    Density-ratio based clustering

    Density-ratio based clustering

    Discovering clusters with varying densities

    This site provides the source code of two approaches for density-ratio based clustering, used for discovering clusters with varying densities. One approach is to modify a density-based clustering algorithm to do density-ratio based clustering by using its density estimator to compute density-ratio. The other approach involves rescaling the given dataset only. An existing density-based clustering algorithm, which is applied to the rescaled dataset, can find all clusters with varying densities that would otherwise impossible had the same algorithm been applied to the unscaled dataset. Reference: Zhu, Y., Ting, K. M., & Carman, M. J. (2016). Density-ratio based clustering for discovering clusters with varying densities. Pattern Recognition. http://www.sciencedirect.com/science/article/pii/S0031320316301571
    Downloads: 0 This Week
    Last Update:
    See Project
  • Our Free Plans just got better! | Auth0 Icon
    Our Free Plans just got better! | Auth0

    With up to 25k MAUs and unlimited Okta connections, our Free Plan lets you focus on what you do best—building great apps.

    You asked, we delivered! Auth0 is excited to expand our Free and Paid plans to include more options so you can focus on building, deploying, and scaling applications without having to worry about your security. Auth0 now, thank yourself later.
    Try free now
  • 5
    Distance Scaling

    Distance Scaling

    A Distance Scaling Method to Improve Density-Based Clustering

    These functions implement a distance scaling method, proposed by Ye Zhu, Kai Ming Ting, and Maia Angelova, "A Distance Scaling Method to Improve Density-Based Clustering", in PAKDD2018 proceedings: https://doi.org/10.1007/978-3-319-93040-4_31.
    Downloads: 0 This Week
    Last Update:
    See Project
  • 6
    ECOC PAK is a C++ Library for the Error Correcting Output Codes classification framework. It supports several coding and decoding strategies as well as several classifiers.
    Downloads: 0 This Week
    Last Update:
    See Project
  • 7
    This project is devoted to the creation of an open source Error-Correcting Output Codes (ECOC) library for the Machine Learning community. The ECOC framework is a powerful tool to deal with multi-class categorization problems.
    Downloads: 0 This Week
    Last Update:
    See Project
  • 8
    EEG Seizure Prediction

    EEG Seizure Prediction

    Seizure prediction from EEG data using machine learning

    The Kaggle-EEG project is a machine learning solution developed for seizure prediction from EEG data, achieving 3rd place in the Kaggle/University of Melbourne Seizure Prediction competition. The repository processes EEG data to predict seizures by training machine learning models, specifically using SVM (Support Vector Machine) and RUS Boosted Tree ensemble models. The framework processes EEG data into features, trains models, and outputs predictions, handling temporal data to ensure accuracy.
    Downloads: 0 This Week
    Last Update:
    See Project
  • 9

    Face Recognition System

    Face Recognition System Matlab source code

    Research on automatic face recognition in images has rapidly developed into several inter-related lines, and this research has both lead to and been driven by a disparate and expanding set of commercial applications. The large number of research activities is evident in the growing number of scientific communications published on subjects related to face processing and recognition. Index Terms: face, recognition, eigenfaces, eigenvalues, eigenvectors, Karhunen-Loeve algorithm.
    Downloads: 0 This Week
    Last Update:
    See Project
  • Grafana: The open and composable observability platform Icon
    Grafana: The open and composable observability platform

    Faster answers, predictable costs, and no lock-in built by the team helping to make observability accessible to anyone.

    Grafana is the open source analytics & monitoring solution for every database.
    Learn More
  • 10
    This project aims to develop and share fast frequent subgraph mining and graph learning algorithms. Currently we release the frequent subgraph mining package FFSM and later we will include new functions for graph regression and classification package
    Downloads: 0 This Week
    Last Update:
    See Project
  • 11

    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 state-of-the-art minutiae-based system when the performance requirement of the application system does not demand a very low false acceptance rate. Finally, we show that the matching performance can be improved by combining the decisions of the matchers based on complementary (minutiae-based and filter-based) fingerprint information. Index Terms: Biometrics, FingerCode, fingerprints, flow pattern, Gabor filters, matching, texture, verification.
    Downloads: 0 This Week
    Last Update:
    See Project
  • 12
    A graphical MatLab framework for estimating the parameters of, modeling and simulating static and dynamic linear and polynomial systems in the errors-in-variables context with the intent of comparing various estimation strategies.
    Downloads: 0 This Week
    Last Update:
    See Project
  • 13

    GI-ICA

    Matlab implementation of GI-ICA and PEGI

    This is a matlab implementation of the GI-ICA algorithm for ICA in the presence of an additive Gaussian noise. The algorithm is discussed in the paper "Fast Algorithms for Gaussian Noise Invariant Independent Component Analysis" by James Voss, Luis Rademacher, and Mikhail Belkin.
    Downloads: 0 This Week
    Last Update:
    See Project
  • 14
    GURLS

    GURLS

    Grand Unified Regularized Least Squares

    GURLS - (Grand Unified Regularized Least Squares) is a software package for training multiclass classifiers based on the Regularized Least Squares (RLS) loss function. The initial version has been designed and implemented in Matlab. Teh current goal is to implement an object-oriented C++ version to allow for a wider distribution of the library within the open-source developers' comunity. Main functionalities already implemented are: * Automatic parameter selection. * Handle massive datasets. * Great modularity, each method can be used independently. * Wide range of optimization routine. Please contact us if you want to join the developers' team or, otherwirse, feel free to download and use the library in your code and send us feedbacks about existing bugs, possible improvements and further developments.
    Downloads: 0 This Week
    Last Update:
    See Project
  • 15

    High-order HMM in Matlab

    Implementation of duration high-order hidden Markov model in Matlab.

    Implementation of duration high-order hidden Markov model (DHO-HMM) in Matlab with application in speech recognition.
    Downloads: 0 This Week
    Last Update:
    See Project
  • 16
    Isolation Similarity

    Isolation Similarity

    aNNE similarity based on Isolation Kernel

    Demo of using aNNE similarity for DBSCAN. Written by Xiaoyu Qin, Monash University, March 2019, version 1.0 This software is under GNU General Public License version 3.0 (GPLv3) This code is a demo of method described by the following publication: Qin, X., Ting, K.M., Zhu, Y. and Lee, V.C., 2019, July. Nearest-neighbour-induced isolation similarity and its impact on density-based clustering. In Proceedings of the AAAI Conference on Artificial Intelligence (Vol. 33, pp. 4755-4762). https://ojs.aaai.org//index.php/AAAI/article/view/4402 Bibtex format: @inproceedings{qin2019nearest, title={Nearest-neighbour-induced isolation similarity and its impact on density-based clustering}, author={Qin, Xiaoyu and Ting, Kai Ming and Zhu, Ye and Lee, Vincent CS}, booktitle={Proceedings of the AAAI Conference on Artificial Intelligence}, volume={33}, pages={4755--4762}, year={2019} }
    Downloads: 0 This Week
    Last Update:
    See Project
  • 17
    Isolation‐based anomaly detection

    Isolation‐based anomaly detection

    Isolation‐based anomaly detection using nearest‐neighbor ensembles

    This site provides the source code of Isolation‐based anomaly detection (iNNE). https://onlinelibrary.wiley.com/doi/abs/10.1111/coin.12156 Bandaragoda, T.R., Ting, K.M., Albrecht, D., Liu, F.T., Zhu, Y. and Wells, J.R., 2018. Isolation‐based anomaly detection using nearest‐neighbor ensembles. Computational Intelligence, 34(4), pp.968-998.
    Downloads: 0 This Week
    Last Update:
    See Project
  • 18
    Kernel Adaptive Filtering Toolbox

    Kernel Adaptive Filtering Toolbox

    a Matlab benchmarking toolbox for kernel adaptive filtering

    [Note: This project has moved. Visit https://github.com/steven2358/kafbox/ for the latest version.] A Matlab benchmarking toolbox for kernel adaptive filtering. Kernel adaptive filtering algorithms are online and adaptive regression algorithms based on kernels. They are suitable for nonlinear filtering, prediction, tracking and nonlinear regression in general. This toolbox includes algorithms, demos, and tools to compare their performance. See the included README file for a list of included algorithms and more details. If you use this toolbox in your research please cite: @inproceedings{vanvaerenbergh2013comparative, author = {Van Vaerenbergh, Steven and Santamar{\'i}a, Ignacio}, booktitle = {2013 IEEE Digital Signal Processing (DSP) Workshop and IEEE Signal Processing Education (SPE)}, title = {A Comparative Study of Kernel Adaptive Filtering Algorithms}, year = {2013}, note = {Software available at \url{https://github.com/steven2358/kafbox/}} }
    Downloads: 0 This Week
    Last Update:
    See Project
  • 19
    Linear Time Invariant (LTI) system identification using particle swarm optimization (PSO) algorithm. Creators : Vahid Kiani, Hadi Sadoghi Yazdi
    Downloads: 0 This Week
    Last Update:
    See Project
  • 20

    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 Vijayakumar, Aaron D'Souza and Stefan Schaal, Incremental Online Learning in High Dimensions, Neural Computation, vol. 17, no. 12, pp. 2602-2634 (2005). [2] Stefan Klanke, Sethu Vijayakumar and Stefan Schaal, A Library for Locally Weighted Projection Regression, Journal of Machine Learning Research (JMLR), vol. 9, pp. 623--626 (2008). More details and usage guidelines on the code website.
    Downloads: 0 This Week
    Last Update:
    See Project
  • 21
    Downloads: 0 This Week
    Last Update:
    See Project
  • 22
    A MATLAB spectral clustering package to handle large data sets (200,000 RCV1 data) on a 4GB memory general machine. We implement various ways of approximating the dense similarity matrix, including nearest neighbors and the Nystrom method.
    Downloads: 0 This Week
    Last Update:
    See Project
  • 23

    MCTIMME

    Microbial Counts Trajectories Infinite Mixture Model Engine

    MCTIMME is a nonparametric Bayesian computational framework for analyzing microbial time-series data.The current implementation is in Matlab.
    Downloads: 0 This Week
    Last Update:
    See Project
  • 24
    Machine Learning Homework

    Machine Learning Homework

    Matlab Coding homework for Machine Learning

    The Machine-Learning-homework repository by user “Ayatans” is a collection of MATLAB code intended to solve or illustrate assignments in machine learning courses. It includes implementations of standard machine learning algorithms (such as regression, classification, etc.), scripts for data loading and preprocessing, and evaluation routines (e.g. accuracy, error metrics). Because it is structured as homework or practice material, the code is likely intended more for didactic use than for production deployment. It may contain comments, example datasets, and perhaps test scripts. The repository does not seem to be heavily maintained as a software project; rather, it functions as a library of solved problems and educational examples. The project is useful if you want working MATLAB examples of classic ML techniques, to study, adapt, or compare with your own implementations.
    Downloads: 0 This Week
    Last Update:
    See Project
  • 25
    This site contains four packages of Mass and mass-based density estimation. 1. The first package is about the basic mass estimation (including one-dimensional mass estimation and Half-Space Tree based multi-dimensional mass estimation). This packages contains the necessary codes to run on MATLAB. 2. The second package includes source and object files of DEMass-DBSCAN to be used with the WEKA system. 3. The third package DEMassBayes includes the source and object files of a Bayesian classifier using DEMass. DEMassBayes.7z has jar file to be used with WEKA and a readme file listing parameters used. The source files are included in DEMassBayes_Source.7z. 4. The four package is MassTER includes source and JAR file to be used with WEKA system..
    Downloads: 0 This Week
    Last Update:
    See Project