Search Results for "matlab code for svm classification"
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Showing 24 open source projects for "matlab code for svm classification"
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MatlabMachine
Machine learning algorithms
Matlab-Machine is a comprehensive collection of machine learning algorithms implemented in MATLAB. It includes both basic and advanced techniques for classification, regression, clustering, and dimensionality reduction. Designed for educational and research purposes, the repository provides clear implementations that help users understand core ML concepts. -
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Machine Learning Octave
MatLab/Octave examples of popular machine learning algorithms
Code written so as to expose and comment on mathematical steps. The repository includes clustering, regression, classification, neural networks, anomaly detection, and other standard ML topics. Does not rely heavily on specialized toolboxes or library shortcuts. -
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dlib
Toolkit for making machine learning and data analysis applications
Dlib is a modern C++ toolkit containing machine learning algorithms and tools for creating complex software in C++ to solve real world problems. It is used in both industry and academia in a wide range of domains including robotics, embedded devices, mobile phones, and large high performance computing environments. Dlib's open source licensing allows you to use it in any application, free of charge. Good unit test coverage, the ratio of unit test lines of code to library lines of code is... -
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Exclusively Dark Image Dataset
ExDARK dataset is the largest collection of low-light images
The Exclusively Dark (ExDARK) dataset is one of the largest curated collections of real-world low-light images designed to support research in computer vision tasks under challenging lighting conditions. It contains 7,363 images captured across ten different low-light scenarios, ranging from extremely dark environments to twilight. Each image is annotated with both image-level labels and object-level bounding boxes for 12 object categories, making it suitable for detection and classification... -
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m2cgen
Transform ML models into a native code
m2cgen (Model 2 Code Generator) - is a lightweight library that provides an easy way to transpile trained statistical models into a native code (Python, C, Java, Go, JavaScript, Visual Basic, C#, PowerShell, R, PHP, Dart, Haskell, Ruby, F#, Rust, Elixir). Transform ML models into a native code (Java, C, Python, Go, JavaScript, Visual Basic, C#, R, PowerShell, PHP, Dart, Haskell, Ruby, F#, Rust) with zero dependencies. Some models force input data to be particular type during prediction phase... -
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Machine-Learning
kNN, decision tree, Bayesian, logistic regression, SVM
Machine-Learning is a repository focused on practical machine learning implementations in Python, covering classic algorithms like k-Nearest Neighbors, decision trees, naive Bayes, logistic regression, support vector machines, linear and tree-based regressions, and likely corresponding code examples and documentation. It targets learners or practitioners who want to understand and implement ML algorithms from scratch or via standard libraries, gaining hands-on experience rather than relying... -
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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. ... -
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ConvNet Burden
Memory consumption and FLOP count estimates for convnets
convnet-burden is a MATLAB toolbox / script collection estimating computational cost (FLOPs) and memory consumption of various convolutional neural network architectures. It lets users compute approximate burdens (in FLOPs, memory) for standard image classification CNN models (e.g. ResNet, VGG) based on network definitions. The tool helps researchers compare the computational efficiency of architectures or quantify resource needs. Estimation of memory consumption (e.g. feature map sizes,... -
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Marine Mammal Acoustic DCL
Advanced acoustic detection, classification and localization
Advanced detection, classification and localization (DCL) of marine mammals and passive acoustic monitoring (PAM). Code is being developed as Matlab routines, interfaces for Ishmael (http://www.bioacoustics.us/ishmael.html), and in other formats. -
Azore is a computational fluid dynamics software tool for analyzing fluid flow and heat transfer.CFD provides engineers and scientists a method for analyzing a wide array of fluid mechanics, thermal, and chemical problems numerically on a computer.
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JSiteDescriptor
Binding site descriptor generation for SVM based classification.
A set of java programs that extract coordinate and chemical information from PDB files. The binding site regions are extracted using grid based scheme. For binding site, spatio-chemical descriptor is generated based on PocketMatch algorithm of Dr. Kalidas (author of this project too). -
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The code consists of an automatic segmentation system that is based on the Hough transform, and is able to localize the circular iris and pupil region, occluding eyelids and eyelashes, and reflections. The extracted iris region was then normalized into a rectangular block with constant dimensions to account for imaging inconsistencies. Finally, the phase data from 1D Log-Gabor filters was extracted and quantized to four levels to encode the unique pattern of the iris into a bit-wise...
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BPL
Bayesian Program Learning model for one-shot learning
...The repository contains code for parsing stroke sequences, fitting motor programs, exemplar generation, classification, re-fitting, and demonstration scripts. -
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classify-20-NG-with-4-ML-Algo
Problem involves classifying 20000 messages into different 20 classes
...Each of these algorithms has its peculiar data format; the specific format and how to reconstruct the entire dataset are illustrated in other sections below. Out of all the methods, SVM using the Libsvm [1] produced the most accurate and optimized result for its classification accuracy for the 20 classes. All the algorithm implementation was written Matlab. Download the code and Report here. -
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Problem Description: 20 newsgroup Classification problem Bayesian learning for classifying net news text articles: Naive Bayes classifiers are among the most successful known algorithms for learning to classify text documents. We will provide a data set containing 20,000 newsgroup messages drawn from the 20 newsgroups. The dataset contains 1000 documents from each of the 20 newsgroups. 1. For classes descriptions, please refer Table 6.3 of Dr. Mitchell's book (Machine Learning, Tom...
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Java Library for Machine Learning
A pure Java library for machine learning
JML is a machine learning library in Java, it is a pure Java package, and thus is cross-platform. The goal of JML is to make machine learning methods very easy to use and speed up code conversion from MATLAB to Java. Please be noted that JML has been replaced by LAML. -
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svmradiusmargin
code for convex formulation of radius-margin based SVM
Matlab code for the novel algorithms presented in the paper Convex formulation for radius-margin based Support Vector Machines -
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GoIFISH
for the semi-automated analysis of IFISH images
GoIFISH has been developed for the analysis of IFISH (Immunofluorescence + Fluorescence in situ Hybridisation) images, performing nuclear, membrane and spot detection. Users can manually edit segmentation results, perform background adjustments, construct heatmaps, topology maps, and perform cell classification. All results can be exported for further analysis. GoIFISH has been developed in MATLAB, however binaries are provided to run the program outside of the MATLAB environment. Source code is also available for download. To cite this software: "GoIFISH: A system for the quantification of single cell heterogeneity from IFISH images" Trinh A, Rye IH, Almendro V, Helland A, Russness HG, Markowetz F May 2014 -
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ConvNetJS
Deep learning in Javascript to train convolutional neural networks
...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 train Convolutional Networks that process images, and experimental Reinforcement Learning modules, based on Deep Q Learning. The library allows you to formulate and solve Neural Networks in Javascript. If you would like to add features to the library, you will have to change the code in src/ and then compile the library into the build/ directory. ... -
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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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Code for Semi-Supervised Machine Learning Techniques, Self-Learning and Co-training used in the paper: Rania Ibrahim, Noha A. Yousri, Mohamed A. Ismail and Nagwa M, El-Makky. “miRNA and Gene Expression based Cancer Classification using Self-Learning and Co-Training Approaches”. IEEE International Conference on Bioinformatics and Biomedicine (BIBM), pp. 495-498, 2013. --------------------------------------------------------------------------- For Self-Learning: java -jar -Xms1700m SelfLearner.jar [trainFile] [testFile] [labelFile] [unlabeledFile] [Alpha] [ClassifierType(randomforest,svm)] [resultFile] [ClassifierModelFile] For Co-Training: java -jar -Xms2500m CoTraining.jar [trainFile-Side1] [testFile-Side1] [labelFile-Side1] [unlabeledFile-Side1] [trainFile-Side2] [testFile-Side2] [labelFile-Side2] [unlabeledFile-Side2] [MappingFile] [Alpha] [ClassifierType(randomforest,svm)] [resultFile] [ClassifierModelFileSide1] [ClassifierModelFileSide2]
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VOC-DPM
Object detection system using deformable part models (DPMs)
The VOC-DPM repository is an implementation of an object detection system built on deformable part models (DPMs) and latent SVM learning, specifically packaged as “voc-release5.” It is the companion code for Ross Girshick’s dissertation, and extends earlier work on discriminatively trained DPMs. The system supports a grammar-based representation for object models, allowing structures such as mixtures and hierarchies to represent parts and whole objects. It implements both latent SVM training... -
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lonestar
A feature selection and classification algorithm based on L1 Norm SVM
A feature selection and classification algorithm. It is based on L1 Norm Support Vector Machine with t-test and Recursive Feature Elimination. -
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Provides pre-compiled MEX functions that wrap around the libsvm C library. Many enhancement are applied to the C version of the library to speed up Matlab usage. 64-bit only.
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OSU SVM is a Support Vector Machine (SVM) toolbox for the MATLAB numerical environment. The toolbox is used to create models for regression and classification using support vector machines.
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