Due to some correlative or repetitive factors between features or samples with high
dimension and large amount of sample data, when traditional back-propagation (BP) neural …

A longitudinal study was used to investigate the quantification of osteoarthritis and prediction
of tibial cartilage loss by analysis of the tibia trabecular bone from magnetic resonance …

High‐dimensional large sample data sets, between feature variables and between samples,
may cause some correlative or repetitive factors, occupy lots of storage space, and consume …

Using the neural network to classify the data which has higher dimension and fewer
samples means overmuch feature inputs influence the structure design of neural network …

Aiming at the small sample with high-feature dimension and few numbers will cause a
serious problem if simply using the traditional Elman neural network to deal with the small …

ABSTRACT Traditional Partial Least Squares Regression (PLSR) models frequently
underperform when handling data characterized by uneven categories. To address the …

We present a texture analysis methodology that combines uncommitted machine-learning
techniques and sparse feature transformation methods in a fully automatic framework. We …