Frontiers

BrainInsights: a comprehensive framework for pre-processing, analysis, and interpretation of neuroimaging data using traditional statistics and machine learning

Neuroimaging presents us with an in-depth understanding about brain structure and function, yet the data complexity poses significant analytical challenges. Current frameworks suffer from issues such ...
Scientific Research Publishing

Cunningham, J.P. and Ghahramani, Z. (2015) Linear Dimensionality Reduction: Survey, in-Sights, and Generalizations. Journal of Machine Learning Research, 16, 2859-2900.

ABSTRACT: The visualization of high-dimensional data is a cornerstone of modern scientific inquiry, particularly in quantum physics, where complex non-linear interactions define system behavior. While ...
PNAS

Identifying variation in dinosaur footprints and classifying problematic specimens via unbiased unsupervised machine learning

Dinosaur footprints are iconic fossils, but it is challenging to identify their makers. This is illustrated by a long-standing debate about whether some footprints from the Late Triassic-Early ...
IEEE

Multispace Dimensionality Reduction Based on Extreme Learning Machine-Assisted Autoencoder for High-Dimensional Expensive Optimization

Abstract: Faced with high-dimensional expensive optimization problems (HEOPs), existing high-dimensional expensive optimization algorithms (HEOAs) struggle to locate promising areas quickly due to a ...
Beebom

What is the Difference Between Deep Learning and Machine Learning

Machine learning requires humans to manually label features while deep learning automatically learns features directly from raw data. ML uses traditional algorithms like decision tress, SVM, etc., ...
Frontiers

Estimating rice yield-related traits using machine learning models integrating hyperspectral and texture features

Rapidly estimating multiple trait indicators simultaneously, nondestructively, and with high precision is an important means of accurate diagnosis in modern phenomics. Increasing the accuracy of ...
Scientific Research Publishing

Van Der Maaten, L., Postma, E. and Van den Herik, J. (2009) Dimensionality Reduction: A Comparative Review. Journal of Machine Learning Research, 10, 66-71.

ABSTRACT: Since transformer-based language models were introduced in 2017, they have been shown to be extraordinarily effective across a variety of NLP tasks including but not limited to language ...