Kernel methods represent a cornerstone in modern machine learning, enabling algorithms to efficiently derive non-linear patterns by implicitly mapping data into high‐dimensional feature spaces ...

The Machine Learning: Practical Applications online certificate course from the London School of Economics and Political Science (LSE) focuses on the practical applications of machine learning in ...

By the end of the course, you will have developed the ability to understand how machine learning can be integrated into current business models and the challenges that this poses. Hear from some of ...

The image semantic classification is new focus in the image classification field, the traditional classification algorithm is based on the low level visual features, but there is an enormous semantic ...

This study explores the development of two predictive models for the yield sooting index (YSI) of various fuels using the advanced capabilities of machine learning (ML), particularly multilayer ...

Kernel PCA extends linear PCA using kernel tricks to reveal non-linear structures. Neural network-based autoencoders are adept at learning complex non-linear relationships.

Summary <p>In machine learning, kernel machines (KMs) are class pattern analysis algorithms. Their purpose is to find and study the types of relations in datasets. KMs receive their name from the fact ...

A comprehensive machine learning project featuring modular implementations for classification and clustering. The project applies ridge regression, nearest neighbors, logistic regression, and ...

LDA is a popular technique for dimensionality reduction in the field of pattern recognition and machine learning. Dataset. The dataset for this project is the AT&T Face ... However, kernel PCA is more ...