Artificial intelligence is rapidly changing the job market, automating jobs across industries. Therefore, in such a scenario, upskilling oneself in industry-relevant AI skills becomes even more ...
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) ...
Abstract: In the construction of machine learning model, when the input data dimension is too large and the data characteristics are particularly complex, the complexity of the model will increase, ...
Are you only seeing posts on your social media feeds that you agree with? You might be stuck in an echo chamber. This lesson will teach students about algorithms, confirmation bias and how to avoid ...
This project is an implementation of Principal Component Analysis (PCA) in Python. PCA is a technique for dimensionality reduction and data visualization that aims to find the most important ...
Implementing PCA (Principal Component Analysis) from scratch for Dimensionality Reduction which is Reducing the number of input variables for a predictive model ...
In this paper, the authors discuss some of the popular face recognition algorithms. Face recognition is widely used biometric technique at many places like international airports, gaming industries ...
ABSTRACT: Dimension reduction is defined as the processes of projecting high-dimensional data to a much lower-dimensional space. Dimension reduction methods variously applied in regression, ...
ABSTRACT: Tracking and segmentation of moving objects are suffering from many problems including those caused by elimination changes, noise and shadows. A modified algorithm for the adaptive ...
Some results have been hidden because they may be inaccessible to you
Show inaccessible results