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adaboost

A simple adaboost code using decision stumps as weak classifiers

adaboost-1

Boosting is a class of machine learning methods based on the idea that a combination of simple classifiers (obtained by a weak learner) can perform better than any of the simple classifiers alone.

adaboost-2

The Adaboost method for creating a strong binary classifier from a series of weak classifiers is implemented. Classification results are shown for some synthetic datasets and the MNIST dataset containing images of digits.

adaboost-decisionstump

Implementation of Adaboost with decision stump classifiers.

adaboost-for-liver-disorder-classification

Adaptive Boosting with Decision Stumps for the BUPA Liver Disorder Data Set

adaboostexample

Implementation of the AdaBoost classifier using Matlab.

adaboosttools

simple tools in Matlab for adaboost

adnet

Attention-guided CNN for image denoising(Neural Networks,2020)

aerial-feature-extraction-using-deep-nets

Feature Extraction Visualization with Classification and Segmentation using Pre-trained DeepNets and U-Net

algorithm

涵盖LeetCode、剑指offer、手撕代码高频算法题、ML重点知识点以及概率智力题等

alzhiemers-disease-classification

This project aims to perform a supervised learning based two-class classification of the Alzheimer's patients (AD vs MCI, AD vs CTL and MCI vs CTL) using a multi-feature fusion algorithm for feature selection and an ensemble of classifiers for classification. The leave-ten-out cross validation technique is used to create multiple splits of the dataset. The accuracy results of this proposed model are then evaluated with respect to other state-of-the models on this problem domain.

bagging-boosting-random-forests

Bagging, boosting and random forests in Matlab

bagging-randomforest-adaboost

Implementation of bagging/randomforest/adaboost for number classification

basic4ai

机器学习、深度学习、自然语言处理等人工智能基础知识总结。

bcdu-net

BCDU-Net : Medical Image Segmentation

bilibili346623

Examples shown in Bilibili Live 346623

breast-cancer-classification

Breast Cancer Classification using CNN and transfer learning

breast-cancer-detection-at-early-stage-using-machine-learning-techniques-on-mammograms

Breast Cancer is the world's Second Cause of Death. A delayed detection of cancerous tissue growth in a patient is the key reason for this increased death rate. Up to 60 per cent of breast cancer patients are diagnosed in later stages. Our paper's main purpose is to develop an image processing algorithm with the help of MATLAB and by classifying it using machine learning techniques for earlier breast cancer detection. The obtained mammogram images are used as input data.Pre-processing of input images is achieved by applying modified CLAHE techniques to improve the quality of the images. The gray threshold algorithm is used to remove pectoral muscles in a mammogram.feature extraction is performed in a matlab and these texture parameters are then used to classify various techniques in machine learning.In testing phase, after completion of image processing steps such as Pre-processing and extraction of features, the statistic parameters are given to the classifier as input. The classifier's performance is made up of two classes, usual and abnormal respectively. The machine learning algorithm is developed in python language. The processing time for Genuine case testing and confirmation is very low. A 82 per cent accuracy rate is achieved using logistic regression classifiers.

breast_mri_fusion

[CVAMD 2021] "End-to-End Learning of Fused Image and Non-Image Feature for Improved Breast Cancer Classification from MRI"

c2l_miccai2020

capsnet-collections

A collection of CapsNet implementations (Dynamic Routing Between Capsules, Sara Sabour, Nicholas Frosst, Geoffrey E Hinton, NIPS 2017 )

carnd-alexnet-feature-extraction

This lab guides you through using AlexNet and TensorFlow to build a feature extraction network.

ccafuse

Feature fusion using Canonical Correlation Analysis (CCA)

character-image-recognition-matlab

Image Preprocessing (Binarization), Histogram of Oriented Gradients (HOG) feature extraction, SVM tuning va grid search (Kernel, Kernel Scale, BoxConstraint), classification analysis (Confusion Matrix )

clashx

class-project

Deep-Learning Class Project

classify-it

Face recognition using various classifiers

coderelease_colornormalization

This is the MATLAB code for stain separation and color normalization in computational pathology (histopathological images)

colon

Colorectal Cancer Classification using Deep Convolutional Networks

colorectal-cancer-classification-with-histopathological-images