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general-purpose-genp-bioimage-ensemble-of-handcrafted-and-learned-features-with-data-augmentation

General Purpose (GenP) Bioimage Ensemble of Handcrafted and Learned Features with Data Augmentation

gensegnet

Generative ConvNet for Image Segmentation

gm_augmentation

Repository related to 'Data augmentation based on generative model' paper

gmm-classifier

Gaussian Mixture Model (GMM) classifier in Matlab.

gmm_image_denoising

Image Denoising Using Unsupervised Learning of Different Gaussian Mixture Models

golfs

The python tools for GOLFS: Feature Selection via Combining Both Global and Local Information for High Dimensional Clustering

grains

GRAINS: Generative Recursive Autoencoders for INdoor Scenes

graphembedding_dr_elm

Graph embedding based dimension reduction with ELM

graphlearning

SJTU,graduate project,semi-supervised based on graph

handling-imbalanced-data

Analysis of techniques for handling imbalance between classes, using Credit card fraud detection dataset.

hash-learning.github.io

hashing-baseline-for-image-retrieval

:octocat:Various hashing methods for image retrieval and serves as the baselines

hb-on-cnn-features-for-scenes

Hierarchical Bayes classifier working in the space of features produced by convolutional neural network for the images of scenes

health-outcome-prediction

Build models using tweets data and demographic data, including pca, KNN, linear regression, lasso regression, svm, random forests, bagging, boosting etc.

heart_attack_prediction-using_feature-selection_algorithm

Heart Attack Prediction by implementing Feature Selection such as SelectKBest & Recursive Feature Elimination

hierarchical-elm-network

IJCNN 2015 Hierarchical extreme learning machine for unsupervised representation learning

hmm-moment-classification

Generative classification of discrete time series using Hidden Markov Models and a composite likelihood based on third-order moments

hog-pedestrian-detector

MATLAB implementation of a basic HOG + SVM pedestrian detector.

hog_matlab

Matlab implementation of the HOG descriptor for pedestrian detection

human-iris-detection

Using ASMs to detect human iris. Active shape models (ASMs) are statistical models of the shape of objects which iteratively deform to fit to an example of the object in a new image, developed by Tim Cootes and Chris Taylor in 1995.The shapes are constrained by the PDM (point distribution model) Statistical Shape Model to vary only in ways seen in a training set of labelled examples. The shape of an object is represented by a set of points (controlled by the shape model). The ASM algorithm aims to match the model to a new image.

image-classification

Image Classification via Bag of Visual words or CNN

image-colorization-using-content-based-image-retrieval

image-dictionary-learning-nmf-and-k-svd

Matlab/Octave scripts for learning image dictionary using Non-negative Matrix Factorization and K-SVD

image-segmentation

Image segmentation, feature extraction, and SVM classification

image_classification_with_autoencoders

imagereconstruction-musicclassification_svd-fitcecoc

Utilizing principle component analysis to extract primary features from songs and images, identifying artists/genres using SVM + fitcecoc on SVD of SFFT Spectrograms

imagesegmentation

kmeans,fcm,kfcm实现图像分割

imbalanced-data-set-classification-in-bci-and-neural-decoding

An SVM Ensemble for Imbalanced Data Set Classification in Brain-Computer Interfaces Data

imbalanced_classification

A cost-sensitive learning approach for handling classification with unbalanced data

imbalanced_data-gfrnn_classifier

GFRNN Classifier for imbalanced datasets