Analytics Insight

Machine Learning, Stanford University

Stanford University’s Machine Learning (XCS229) is a 100% online, instructor-led course offered by the Stanford School of ...
Hosted on MSN

Adagrad algorithm explained and implemented from scratch in Python

Learn the Adagrad optimization algorithm, how it works, and how to implement it from scratch in Python for machine learning models. #Adagrad #Optimization #Python Why presidents stumble in this most ...
IEEE

Investigation on PCA and LDA Feature Extraction Algorithms with KNN Classifier Targetting to Improve Diagnostic Accuracy into Detection of Breast Cancer Malignancy

Abstract: This work aims to compare two different Feature Extraction Algorithms (FEAs) viz. Principal Component Analysis (PCA) and Linear Discriminant Analysis (LDA), using a K-Nearest Neighbor (KNN) ...
Scientific Research Publishing

Lin, J. and He, J. (2022) Parallel Random Forest Prediction Algorithm Based on PCA Stratified Sampling in the Big Data Environment. China Management Informationization, 25, 172 ...

ABSTRACT: To improve the efficiency of air quality analysis and the accuracy of predictions, this paper proposes a composite method based on Vector Autoregressive (VAR) and Random Forest (RF) models.
Frontiers

Identifying network state-based Parkinson’s disease subtypes using clustering and support vector machine models

Our findings suggest that, while PD is generally associated with a larger DAT deficit in specific brain structures of the neostriatum, it exhibits intrinsic heterogeneity across individuals, which may ...
IEEE

Design and Implementation of an Efficient Parallel Algorithm for Sparse Principal Component Analysis

Abstract: Sparse matrix computations are an important class of algorithms. One of the important topics in this field is SPCA (Sparse Principal Component Analysis), a variant of PCA. SPCA is used to ...