Supervised learning (Machine learning)

Training data for machine learning : = human supervision from annotation to data science / by: (Language materials, printed)

Fundamentals of supervised machine learning = with applications in Python, R, and Stata / by: (Language materials, printed)

Supervised machine learning = optimization framework and applications with SAS and R / by: (Language materials, printed)

Boosting : = foundations and algorithms / by: (Language materials, printed)

Supervised learning with complex-valued neural networks by: (Language materials, printed)

The elements of statistical learning = data mining, inference, and prediction / by: (Language materials, printed)

Graph-based semi-supervised learning by: (Language materials, printed)

Large scale hierarchical classification = state of the art / by: (Language materials, printed)

Boosting (Algorithms)

Machine learning.

Statistics in Social Sciences, Humanities, Law, Education, Behavorial Sciences, Public Policy.

Computer Appl. in Life Sciences.

SAS (Computer program language)

Artificial Intelligence (incl. Robotics)

R (Computer program language)

Computational Intelligence.

Engineering.

Statistical Learning.

Statistics.

Supervised learning (Machine learning)

Statistical Software.

Signal, Image and Speech Processing.

Neural networks (Computer science)

Statistics for Engineering, Physics, Computer Science, Chemistry & Geosciences.

Program transformation (Computer programming)

Machine learning

Biostatistics.

Data mining.

Statistics in Business, Management, Economics, Finance, Insurance.

Machine Learning.

Computational Biology/Bioinformatics.

Statistical Theory and Methods.

Data Mining and Knowledge Discovery.