AIMC Topic: Discriminant Analysis

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Automatic Target Recognition Strategy for Synthetic Aperture Radar Images Based on Combined Discrimination Trees.

Computational intelligence and neuroscience
A strategy is introduced for achieving high accuracy in synthetic aperture radar (SAR) automatic target recognition (ATR) tasks. Initially, a novel pose rectification process and an image normalization process are sequentially introduced to produce i...

A Hybrid Semi-Supervised Anomaly Detection Model for High-Dimensional Data.

Computational intelligence and neuroscience
Anomaly detection, which aims to identify observations that deviate from a nominal sample, is a challenging task for high-dimensional data. Traditional distance-based anomaly detection methods compute the neighborhood distance between each observatio...

Supervised learning techniques and their ability to classify a change of direction task strategy using kinematic and kinetic features.

Journal of biomechanics
This study examines the ability of commonly used supervised learning techniques to classify the execution of a maximum effort change of direction task into predefined movement pattern as well as the influence of fuzzy executions and the impact of sel...

Self-Taught Learning Based on Sparse Autoencoder for E-Nose in Wound Infection Detection.

Sensors (Basel, Switzerland)
For an electronic nose (E-nose) in wound infection distinguishing, traditional learning methods have always needed large quantities of labeled wound infection samples, which are both limited and expensive; thus, we introduce self-taught learning comb...

Combination of minimum enclosing balls classifier with SVM in coal-rock recognition.

PloS one
Top-coal caving technology is a productive and efficient method in modern mechanized coal mining, the study of coal-rock recognition is key to realizing automation in comprehensive mechanized coal mining. In this paper we propose a new discriminant a...

Factors analysis of protein O-glycosylation site prediction.

Computational biology and chemistry
To improve the prediction accuracy of O-glycosylation sites, and analyze the structure of the O-glycosylation sites, factor analysis based prediction is proposed in this study. Our studies show that factor analysis strongly boosts machine learning al...

Comparative approaches for classification of diabetes mellitus data: Machine learning paradigm.

Computer methods and programs in biomedicine
BACKGROUND AND OBJECTIVE: Diabetes is a silent killer. The main cause of this disease is the presence of excessive amounts of metabolites such as glucose. There were about 387 million diabetic people all over the world in 2014. The financial burden o...

Optimisation of a machine learning algorithm in human locomotion using principal component and discriminant function analyses.

PloS one
Assessment methods in human locomotion often involve the description of normalised graphical profiles and/or the extraction of discrete variables. Whilst useful, these approaches may not represent the full complexity of gait data. Multivariate statis...

Sex determination from the femur in Portuguese populations with classical and machine-learning classifiers.

Journal of forensic and legal medicine
The assessment of sex is of paramount importance in the establishment of the biological profile of a skeletal individual. Femoral relevance for sex estimation is indisputable, particularly when other exceedingly dimorphic skeletal regions are missing...

Classification of caesarean section and normal vaginal deliveries using foetal heart rate signals and advanced machine learning algorithms.

Biomedical engineering online
BACKGROUND: Visual inspection of cardiotocography traces by obstetricians and midwives is the gold standard for monitoring the wellbeing of the foetus during antenatal care. However, inter- and intra-observer variability is high with only a 30% posit...